Single-cell-eQTL mapping in circulating immune cells reveals genetic regulation of response-associated networks in lung cancer immunotherapy

IF 24.9 1区 医学 Q1 ONCOLOGY
Hyungtai Sim, Geun-Ho Park, Woong-Yang Park, Se-Hoon Lee, Murim Choi
{"title":"Single-cell-eQTL mapping in circulating immune cells reveals genetic regulation of response-associated networks in lung cancer immunotherapy","authors":"Hyungtai Sim,&nbsp;Geun-Ho Park,&nbsp;Woong-Yang Park,&nbsp;Se-Hoon Lee,&nbsp;Murim Choi","doi":"10.1002/cac2.70042","DOIUrl":null,"url":null,"abstract":"<p>While immune checkpoint inhibitors (ICIs) are adopted as standard therapy for advanced non-small cell lung cancer (NSCLC), genetic determinants of response heterogeneity remain elusive [<span>1</span>]. As most hematopoietic lineages undergo dynamic changes during tumor pathogenesis and immunotherapy [<span>2</span>], elucidating how germline variants modulate their transcriptomes is critical. Expression quantitative trait loci (eQTL) analysis, especially integrated with single-cell RNA sequencing (scRNA-seq), enables gene regulation mapping at single-cell resolution [<span>3, 4</span>]. Detailed methodologies are described in the Supplementary Materials.</p><p>To investigate how germline variants shape immune gene regulation during ICI treatment, we performed single-cell-eQTL (sc-eQTL) analysis and transcriptomic network profiling. Peripheral blood mononuclear cells (PBMCs) were collected from 73 NSCLC patients treated with anti-programmed cell death protein-1 (PD-1) or programmed death-ligand 1 (PD-L1) therapy, at both baseline and 1-5 weeks post-treatment (Figure 1A, Supplementary Table S1). By integrating scRNA-seq with SNP array data, we analyzed cell-type-resolved sc-eQTLs and gene networks (Figure 1A-B).</p><p>After quality control and pseudobulk aggregation, we identified 9,147 eQTL pairs—expression-regulating SNPs (eSNPs) linked to 3,616 blood expression-regulated genes (eGenes)—across eight immune cell clusters and treatment conditions (Figure 1B-C, Supplementary Figure S1A, Supplementary Table S2). Consistent with previous studies [<span>3, 4</span>], eGene counts correlated with cell abundance, and eSNPs were enriched in regulatory elements (Supplementary Figure S1B-D). Multiadaptive shrinkage [<span>5</span>] revealed distinct cell-type- and treatment-dependent regulation, including 245 treatment-specific eQTLs (Supplementary Figure S2A-D, Supplementary Tables S3-S4). For instance, tumor necrosis factor (<i>TNF</i>) was regulated in monocytes post-treatment (posterior <i>β</i> = 1.17), while TNF receptor 1A (<i>TNFRSF1A</i>) was baseline-regulated in CD8<sup>+</sup> T cells (<i>β</i> = 1.10), indicating genetic variants may shape immune gene expression during ICI therapy (Figure 1D). Additional examples include key cytotoxic mediators perforin 1 (<i>PRF1</i>) and granzyme B (<i>GZMB</i>) in baseline CD8<sup>+</sup> T cells (Figure 1D, Supplementary Figure S2E).</p><p>To validate our findings, we conducted two complementary analyses. First, colocalization analyses with genome-wide association study (GWAS) loci for autoimmune and blood traits showed overlaps (PP.H4 &gt; 0.6), suggesting possible shared regulatory mechanisms (Supplementary Figure S3, Supplementary Tables S5-S6). Second, comparison with external eQTL studies showed our study-specific eQTLs, hereafter referred to as lung cancer-specific eQTLs, were enriched in cancer- and immune response-related pathways (Figure 1E, Supplementary Figure S4A), reflecting chronic immune adaptation. Nevertheless, most regulatory patterns seen in healthy donors from the 1M-scBloodNL study [<span>3</span>] were preserved (Supplementary Figure S4B, Supplementary Tables S7-S8).</p><p>Beyond eQTL mapping, we used weighted gene co-expression network analysis (WGCNA) to build co-expression networks for each major immune cell type [<span>6</span>], enabling comparison of cell-type-specific regulation and transcriptomes (Supplementary Tables S9-S10). Among the identified modules, the CD8-brown module showed elevated baseline activity in the non-durable clinical benefits (NCB) group (Figure 1F, Supplementary Figure S5A). This module was cytotoxic-gene-rich (<i>PRF1</i>, apolipoprotein B mRNA-editing enzyme, catalytic subunit 3G [<i>APOBEC3G</i>], and <i>GZMB</i>) and highly expressed in the NCB group, particularly in differentiated CD8⁺ T subclusters (Supplementary Figure S5B-D). Its activity was supported by external blood datasets from healthy individuals, tumor patients, and ICI-treated tumor datasets (Supplementary Figure S5E-F).</p><p>To explore potential regulators, we applied single-cell regulatory network inference and clustering (SCENIC) [<span>7</span>], identifying eomesodermin (<i>EOMES</i>) and t-box transcription factor 21 (<i>TBX21</i>) as putative regulators linked to CD8<sup>+</sup> T cell differentiation and exhaustion [<span>8</span>]. Their regulon activity aligned with both the CD8-brown module (Figure 1G, Supplementary Figure S6A-B), and the abundance of effector memory CD8<sup>+</sup> T (CD8<sup>+</sup> TEM) cells (Supplementary Figure S6C-D). Core genes, including <i>EOMES</i>, <i>TBX21</i>, and interleukin-2 receptor, beta subunit (<i>IL2RB</i>), were up-regulated in NCB compared to durable clinical benefit (DCB) group (Supplementary Figure S6E). Notably, <i>TBX21</i> and <i>EOMES</i> were not eGenes in our analysis.</p><p>Building on these findings, we employed a graph neural network (GNN)-based framework to refine the CD8-brown module by integrating protein-protein interaction (PPI) networks, gene ontology (GO) annotations, and co-expression data (Supplementary Figure S7A-B). The refined subnetwork captured a more coherent CD8⁺ T cell differentiation program and showed stronger topological connectivity than the original WGCNA-derived gene set in Search Tool for the Retrieval of Interacting Genes/Proteins (STRING; node density: 0.333 vs 0.183; Supplementary Figure S7C-D, Supplementary Table S11) [<span>9</span>]. Pathway enrichment for canonical pathways in CD8<sup>+</sup> T cells supported this refinement (Supplementary Figure S7E-F).</p><p>This module was well-conserved across external scRNA-seq datasets, including the tumor microenvironment (TME) of ICI-treated NSCLC patients (Supplementary Figure S8) [<span>10</span>] and blood from cancer patients (Supplementary Figure S9). Its activation was significantly enhanced in non-responders, particularly in differentiated CD8<sup>+</sup> T cell subclusters like CD8<sup>+</sup> TEM (Figure 1H, Supplementary Figures S8-S9, Supplementary Table S12), suggesting that this systemic cytotoxic signature is recapitulated in the tumor microenvironment and may reflect ICI response-associated immune status.</p><p>Given its strong association with ICI response, we evaluated whether the refined core genes from the CD8-brown module could explain survival outcomes. We derived a core gene score from 15 GNN-prioritized CD8-brown genes with high network centrality (Supplementary Methods, Supplementary Figure S7, Supplementary Table S13), which strongly correlated with module activity (Supplementary Figure S10A). Using grid search and bootstrapping (Supplementary Figure S10B-C), we determined a threshold to classify patients: those with &gt;75% of CD8<sup>+</sup> T cells with high CD8-brown core gene score (&gt;0.36) displayed significantly shorter overall survival (OS, <i>P</i> = 0.035) and progression-free survival (PFS, <i>P</i> = 0.018; Figure 1I, Supplementary Figure S10B-C). The survival association remained significant in a multivariate Cox model adjusting for clinical covariates (Supplementary Figure S10D). Overall, our analysis suggests that the <i>TBX21</i>-<i>EOMES</i> regulatory axis may drive the CD8-brown module, representing CD8<sup>+</sup> T differentiation with cytotoxic eGenes like <i>PRF1</i> and <i>GZMB</i>, which in turn stratify poor responders to ICI.</p><p>Finally, to evaluate how germline variants influence the immune network associated with ICI response, we examined the distribution of eQTLs within co-expression and regulatory networks (Figure 1J-L, Supplementary Figure S11A-C). eGenes were enriched in central network positions (Supplementary Figure S11B); however, no single eQTL–including <i>PRF1, GZMB</i> or <i>TNFRSF1A</i>–exerted a dominant regulatory effect on the CD8-brown module. Instead, genes with higher centrality or WGCNA-assignment were linked to smaller eQTL effect sizes (Figure 1K-L), suggesting functional constraints on key immune differentiation pathways.</p><p>Despite these insights, several limitations exist. First, dichotomizing responses into DCB and NCB may obscure heterogeneous outcomes, while the treatment-induced transcriptomic changes appeared smaller than inter-sample heterogeneity (Supplementary Figure S12). Second, although monocyte-specific eQTL were detected and monocytes-ICI interactions are well-known, we excluded them from downstream analysis due to minimal contribution to ICI-associated co-expression modules (Supplementary Figure S13). Third, our focus on cell-type- and condition-specific eQTLs and co-expression networks may overlook intercellular interactions and trans effects influencing immune function. Lastly, although the CD8-brown core gene score stratified survival in our cohort (c-index = 0.68-0.71 for PFS/OS, Supplementary Figure S10D), its predictive value requires validation in independent datasets.</p><p>Taken together, our study identified 3,616 blood- and 702 lung cancer-specific eGenes via sc-eQTL mapping and revealed a transcriptomic module (CD8-brown) associated with CD8<sup>+</sup> T cell differentiation and non-responder for ICI response. The limited influence of eQTLs on co-expression networks suggests functional constraints in the immune transcriptome. By linking genetic variation to cytotoxic network activity and clinical outcomes, our analysis provides a framework to understand systemic immunity under ICI treatment in metastatic NSCLC. Future research involving larger cohorts and experimental validation of key regulatory variants—such as those affecting <i>PRF1</i> and <i>GZMB</i>—could clarify causal mechanisms and refine personalized ICI strategies.</p><p><b>Hyungtai Sim</b>: Data curation; formal analysis; validation; investigation; writing—original draft. <b>Geun-Ho Park</b>: Data curation; formal analysis; investigation. <b>Woong-Yang Park</b>: Investigation. <b>Se-Hoon Lee</b>: Conceptualization; investigation; writing—original draft. <b>Murim Choi</b>: Conceptualization; investigation; writing—original draft.</p><p>The authors declare no competing interests.</p><p>This work was supported in part by grants from the Korean Research Foundation (NRF-2021R1A2C3014067, NRF-RS-2023-00207857 to Murim Choi, and NRF-2020R1A2C3006535, NRF-RS-2025-00519956 to Se-Hoon Lee), the Korea Health Technology R&amp;D Project through the Korea Health Industry Development Institute (HR20C0025 to Se-Hoon Lee), and Future Medicine 20*30 Project of the Samsung Medical Center (SMX1230041 to Se-Hoon Lee).</p><p>This study investigated human samples and was given permission by the Samsung Medical Center Institutional Review Board (No. 2018-04-048, 2022-01-094). 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引用次数: 0

Abstract

While immune checkpoint inhibitors (ICIs) are adopted as standard therapy for advanced non-small cell lung cancer (NSCLC), genetic determinants of response heterogeneity remain elusive [1]. As most hematopoietic lineages undergo dynamic changes during tumor pathogenesis and immunotherapy [2], elucidating how germline variants modulate their transcriptomes is critical. Expression quantitative trait loci (eQTL) analysis, especially integrated with single-cell RNA sequencing (scRNA-seq), enables gene regulation mapping at single-cell resolution [3, 4]. Detailed methodologies are described in the Supplementary Materials.

To investigate how germline variants shape immune gene regulation during ICI treatment, we performed single-cell-eQTL (sc-eQTL) analysis and transcriptomic network profiling. Peripheral blood mononuclear cells (PBMCs) were collected from 73 NSCLC patients treated with anti-programmed cell death protein-1 (PD-1) or programmed death-ligand 1 (PD-L1) therapy, at both baseline and 1-5 weeks post-treatment (Figure 1A, Supplementary Table S1). By integrating scRNA-seq with SNP array data, we analyzed cell-type-resolved sc-eQTLs and gene networks (Figure 1A-B).

After quality control and pseudobulk aggregation, we identified 9,147 eQTL pairs—expression-regulating SNPs (eSNPs) linked to 3,616 blood expression-regulated genes (eGenes)—across eight immune cell clusters and treatment conditions (Figure 1B-C, Supplementary Figure S1A, Supplementary Table S2). Consistent with previous studies [3, 4], eGene counts correlated with cell abundance, and eSNPs were enriched in regulatory elements (Supplementary Figure S1B-D). Multiadaptive shrinkage [5] revealed distinct cell-type- and treatment-dependent regulation, including 245 treatment-specific eQTLs (Supplementary Figure S2A-D, Supplementary Tables S3-S4). For instance, tumor necrosis factor (TNF) was regulated in monocytes post-treatment (posterior β = 1.17), while TNF receptor 1A (TNFRSF1A) was baseline-regulated in CD8+ T cells (β = 1.10), indicating genetic variants may shape immune gene expression during ICI therapy (Figure 1D). Additional examples include key cytotoxic mediators perforin 1 (PRF1) and granzyme B (GZMB) in baseline CD8+ T cells (Figure 1D, Supplementary Figure S2E).

To validate our findings, we conducted two complementary analyses. First, colocalization analyses with genome-wide association study (GWAS) loci for autoimmune and blood traits showed overlaps (PP.H4 > 0.6), suggesting possible shared regulatory mechanisms (Supplementary Figure S3, Supplementary Tables S5-S6). Second, comparison with external eQTL studies showed our study-specific eQTLs, hereafter referred to as lung cancer-specific eQTLs, were enriched in cancer- and immune response-related pathways (Figure 1E, Supplementary Figure S4A), reflecting chronic immune adaptation. Nevertheless, most regulatory patterns seen in healthy donors from the 1M-scBloodNL study [3] were preserved (Supplementary Figure S4B, Supplementary Tables S7-S8).

Beyond eQTL mapping, we used weighted gene co-expression network analysis (WGCNA) to build co-expression networks for each major immune cell type [6], enabling comparison of cell-type-specific regulation and transcriptomes (Supplementary Tables S9-S10). Among the identified modules, the CD8-brown module showed elevated baseline activity in the non-durable clinical benefits (NCB) group (Figure 1F, Supplementary Figure S5A). This module was cytotoxic-gene-rich (PRF1, apolipoprotein B mRNA-editing enzyme, catalytic subunit 3G [APOBEC3G], and GZMB) and highly expressed in the NCB group, particularly in differentiated CD8⁺ T subclusters (Supplementary Figure S5B-D). Its activity was supported by external blood datasets from healthy individuals, tumor patients, and ICI-treated tumor datasets (Supplementary Figure S5E-F).

To explore potential regulators, we applied single-cell regulatory network inference and clustering (SCENIC) [7], identifying eomesodermin (EOMES) and t-box transcription factor 21 (TBX21) as putative regulators linked to CD8+ T cell differentiation and exhaustion [8]. Their regulon activity aligned with both the CD8-brown module (Figure 1G, Supplementary Figure S6A-B), and the abundance of effector memory CD8+ T (CD8+ TEM) cells (Supplementary Figure S6C-D). Core genes, including EOMES, TBX21, and interleukin-2 receptor, beta subunit (IL2RB), were up-regulated in NCB compared to durable clinical benefit (DCB) group (Supplementary Figure S6E). Notably, TBX21 and EOMES were not eGenes in our analysis.

Building on these findings, we employed a graph neural network (GNN)-based framework to refine the CD8-brown module by integrating protein-protein interaction (PPI) networks, gene ontology (GO) annotations, and co-expression data (Supplementary Figure S7A-B). The refined subnetwork captured a more coherent CD8⁺ T cell differentiation program and showed stronger topological connectivity than the original WGCNA-derived gene set in Search Tool for the Retrieval of Interacting Genes/Proteins (STRING; node density: 0.333 vs 0.183; Supplementary Figure S7C-D, Supplementary Table S11) [9]. Pathway enrichment for canonical pathways in CD8+ T cells supported this refinement (Supplementary Figure S7E-F).

This module was well-conserved across external scRNA-seq datasets, including the tumor microenvironment (TME) of ICI-treated NSCLC patients (Supplementary Figure S8) [10] and blood from cancer patients (Supplementary Figure S9). Its activation was significantly enhanced in non-responders, particularly in differentiated CD8+ T cell subclusters like CD8+ TEM (Figure 1H, Supplementary Figures S8-S9, Supplementary Table S12), suggesting that this systemic cytotoxic signature is recapitulated in the tumor microenvironment and may reflect ICI response-associated immune status.

Given its strong association with ICI response, we evaluated whether the refined core genes from the CD8-brown module could explain survival outcomes. We derived a core gene score from 15 GNN-prioritized CD8-brown genes with high network centrality (Supplementary Methods, Supplementary Figure S7, Supplementary Table S13), which strongly correlated with module activity (Supplementary Figure S10A). Using grid search and bootstrapping (Supplementary Figure S10B-C), we determined a threshold to classify patients: those with >75% of CD8+ T cells with high CD8-brown core gene score (>0.36) displayed significantly shorter overall survival (OS, P = 0.035) and progression-free survival (PFS, P = 0.018; Figure 1I, Supplementary Figure S10B-C). The survival association remained significant in a multivariate Cox model adjusting for clinical covariates (Supplementary Figure S10D). Overall, our analysis suggests that the TBX21-EOMES regulatory axis may drive the CD8-brown module, representing CD8+ T differentiation with cytotoxic eGenes like PRF1 and GZMB, which in turn stratify poor responders to ICI.

Finally, to evaluate how germline variants influence the immune network associated with ICI response, we examined the distribution of eQTLs within co-expression and regulatory networks (Figure 1J-L, Supplementary Figure S11A-C). eGenes were enriched in central network positions (Supplementary Figure S11B); however, no single eQTL–including PRF1, GZMB or TNFRSF1A–exerted a dominant regulatory effect on the CD8-brown module. Instead, genes with higher centrality or WGCNA-assignment were linked to smaller eQTL effect sizes (Figure 1K-L), suggesting functional constraints on key immune differentiation pathways.

Despite these insights, several limitations exist. First, dichotomizing responses into DCB and NCB may obscure heterogeneous outcomes, while the treatment-induced transcriptomic changes appeared smaller than inter-sample heterogeneity (Supplementary Figure S12). Second, although monocyte-specific eQTL were detected and monocytes-ICI interactions are well-known, we excluded them from downstream analysis due to minimal contribution to ICI-associated co-expression modules (Supplementary Figure S13). Third, our focus on cell-type- and condition-specific eQTLs and co-expression networks may overlook intercellular interactions and trans effects influencing immune function. Lastly, although the CD8-brown core gene score stratified survival in our cohort (c-index = 0.68-0.71 for PFS/OS, Supplementary Figure S10D), its predictive value requires validation in independent datasets.

Taken together, our study identified 3,616 blood- and 702 lung cancer-specific eGenes via sc-eQTL mapping and revealed a transcriptomic module (CD8-brown) associated with CD8+ T cell differentiation and non-responder for ICI response. The limited influence of eQTLs on co-expression networks suggests functional constraints in the immune transcriptome. By linking genetic variation to cytotoxic network activity and clinical outcomes, our analysis provides a framework to understand systemic immunity under ICI treatment in metastatic NSCLC. Future research involving larger cohorts and experimental validation of key regulatory variants—such as those affecting PRF1 and GZMB—could clarify causal mechanisms and refine personalized ICI strategies.

Hyungtai Sim: Data curation; formal analysis; validation; investigation; writing—original draft. Geun-Ho Park: Data curation; formal analysis; investigation. Woong-Yang Park: Investigation. Se-Hoon Lee: Conceptualization; investigation; writing—original draft. Murim Choi: Conceptualization; investigation; writing—original draft.

The authors declare no competing interests.

This work was supported in part by grants from the Korean Research Foundation (NRF-2021R1A2C3014067, NRF-RS-2023-00207857 to Murim Choi, and NRF-2020R1A2C3006535, NRF-RS-2025-00519956 to Se-Hoon Lee), the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (HR20C0025 to Se-Hoon Lee), and Future Medicine 20*30 Project of the Samsung Medical Center (SMX1230041 to Se-Hoon Lee).

This study investigated human samples and was given permission by the Samsung Medical Center Institutional Review Board (No. 2018-04-048, 2022-01-094). All participants underwent an informed consent process before being enrolled in the study.

Abstract Image

循环免疫细胞中的单细胞eqtl定位揭示了肺癌免疫治疗中反应相关网络的遗传调控。
虽然免疫检查点抑制剂(ICIs)被用作晚期非小细胞肺癌(NSCLC)的标准治疗,但反应异质性的遗传决定因素仍然难以捉摸。由于大多数造血谱系在肿瘤发病和免疫治疗过程中发生动态变化,阐明种系变异如何调节其转录组是至关重要的。表达数量性状位点(eQTL)分析,特别是与单细胞RNA测序(scRNA-seq)相结合,可以实现单细胞分辨率下的基因调控定位[3,4]。详细的方法在补充材料中描述。为了研究ICI治疗期间种系变异如何影响免疫基因调控,我们进行了单细胞eqtl (sc-eQTL)分析和转录组网络分析。73例接受抗程序性细胞死亡蛋白-1 (PD-1)或程序性死亡配体-1 (PD-L1)治疗的NSCLC患者,在基线和治疗后1-5周收集外周血单个核细胞(图1A,补充表S1)。通过整合scRNA-seq和SNP阵列数据,我们分析了细胞类型分辨的sc- eqtl和基因网络(图1A-B)。经过质量控制和伪体聚集,我们在8个免疫细胞簇和治疗条件下鉴定出9147对eQTL -表达调节snp (eSNPs)与3,616个血液表达调节基因(eGenes)相关(图1B-C,补充图S1A,补充表S2)。与先前的研究一致[3,4],eGene计数与细胞丰度相关,eSNPs在调控元件中富集(Supplementary Figure S1B-D)。多适应性收缩[5]显示了不同的细胞类型和治疗依赖性调控,包括245个治疗特异性eqtl(补充图S2A-D,补充表S3-S4)。例如,肿瘤坏死因子(TNF)在治疗后单核细胞中受到调节(后值β = 1.17),而TNF受体1A (TNFRSF1A)在CD8+ T细胞中受到基线调节(β = 1.10),这表明遗传变异可能在ICI治疗期间影响免疫基因表达(图1D)。其他例子包括基线CD8+ T细胞中的关键细胞毒性介质穿孔素1 (PRF1)和颗粒酶B (GZMB)(图1D,补充图S2E)。为了验证我们的发现,我们进行了两项互补分析。首先,自身免疫和血液性状的全基因组关联研究(GWAS)位点共定位分析显示重叠(PP.H4 &gt; 0.6),表明可能存在共同的调控机制(补充图S3,补充表S5-S6)。其次,与外部eQTL研究比较发现,我们的研究特异性eQTL(以下简称肺癌特异性eQTL)在癌症和免疫应答相关通路中富集(图1E, Supplementary Figure S4A),反映了慢性免疫适应。然而,1M-scBloodNL研究中健康供体的大多数调节模式被保留了下来(补充图S4B,补充表S7-S8)。除了eQTL定位,我们使用加权基因共表达网络分析(WGCNA)构建了每种主要免疫细胞类型[6]的共表达网络,从而能够比较细胞类型特异性调控和转录组(补充表S9-S10)。在已确定的模块中,cd8 -棕色模块在非持久临床获益(NCB)组显示出较高的基线活性(图1F,补充图S5A)。该模块富含细胞毒基因(PRF1、载脂蛋白B mrna编辑酶、催化亚基3G [APOBEC3G]和GZMB),在NCB组中高度表达,特别是在分化的CD8 + T亚簇中(Supplementary Figure S5B-D)。它的活性得到了来自健康个体、肿瘤患者和ici治疗肿瘤数据集的外部血液数据集的支持(补充图S5E-F)。为了探索潜在的调节因子,我们应用单细胞调节网络推断和聚类(SCENIC)[7],确定eomesdermin (EOMES)和T -box转录因子21 (TBX21)作为与CD8+ T细胞分化和衰竭[8]相关的推定调节因子。它们的调节活性与CD8-brown模块(图1G,补充图S6A-B)和效应记忆CD8+ T (CD8+ TEM)细胞的丰度(补充图S6C-D)一致。与持久临床获益(DCB)组相比,NCB组的核心基因,包括EOMES、TBX21和白细胞介素-2受体β亚基(IL2RB)上调(补充图S6E)。值得注意的是,TBX21和EOMES在我们的分析中不是eGenes。在这些发现的基础上,我们采用了基于图神经网络(GNN)的框架,通过整合蛋白质-蛋白质相互作用(PPI)网络、基因本体(GO)注释和共表达数据来完善CD8-brown模块(补充图S7A-B)。 改进后的子网络捕获了更连贯的CD8 + T细胞分化程序,并且比Search Tool for Retrieval of Interacting Genes/Proteins (STRING;节点密度:0.333 vs 0.183; Supplementary Figure S7C-D, Supplementary Table S11)[9]中原始的wgna衍生基因集具有更强的拓扑连通性。CD8+ T细胞中典型通路的富集支持了这一改进(补充图S7E-F)。该模块在外部scRNA-seq数据集中具有良好的保守性,包括ci治疗的非小细胞肺癌患者的肿瘤微环境(TME)(补充图S8)[10]和癌症患者的血液(补充图S9)。它的激活在无应答者中显著增强,特别是在分化的CD8+ T细胞亚群中,如CD8+ TEM(图1H,补充图S8-S9,补充表S12),这表明这种系统性细胞毒性特征在肿瘤微环境中重现,可能反映了ICI应答相关的免疫状态。鉴于其与ICI反应的强烈关联,我们评估了来自CD8-brown模块的精炼核心基因是否可以解释生存结果。我们从15个gnn优先的CD8-brown基因中获得了一个核心基因评分,这些基因具有高网络中心性(补充方法,补充图S7,补充表S13),这与模块活性密切相关(补充图S10A)。使用网格搜索和引导(补充图S10B-C),我们确定了一个阈值来对患者进行分类:具有&gt;75% CD8+ T细胞且CD8-brown核心基因评分高(&gt;0.36)的患者显示出明显较短的总生存期(OS, P = 0.035)和无进展生存期(PFS, P = 0.018;图1I,补充图S10B-C)。在调整临床协变量的多变量Cox模型中,生存相关性仍然显著(补充图S10D)。总的来说,我们的分析表明TBX21-EOMES调控轴可能驱动CD8-brown模块,代表CD8+ T与细胞毒性eGenes(如PRF1和GZMB)分化,从而对ICI产生不良反应。最后,为了评估种系变异如何影响与ICI应答相关的免疫网络,我们检查了eqtl在共表达和调控网络中的分布(图1J-L,补充图S11A-C)。eGenes在中心网络位置富集(补充图S11B);然而,没有单一的eqtl(包括PRF1、GZMB或tnfrsf1a)对CD8-brown模块发挥显性调控作用。相反,具有较高中心性或wgna -配位的基因与较小的eQTL效应大小相关(图k - l),表明对关键免疫分化途径的功能限制。尽管有这些见解,但仍存在一些限制。首先,将反应分为DCB和NCB可能会掩盖异质性结果,而治疗诱导的转录组变化似乎小于样本间异质性(补充图S12)。其次,虽然检测到了单核细胞特异性的eQTL,并且单核细胞- ici相互作用是众所周知的,但由于它们对ici相关的共表达模块的贡献很小,我们将它们排除在下游分析之外(补充图S13)。第三,我们对细胞类型和条件特异性的eqtl和共表达网络的关注可能忽略了影响免疫功能的细胞间相互作用和反式效应。最后,尽管CD8-brown核心基因评分对我们队列中的生存率进行了分层(PFS/OS的c-index = 0.68-0.71, Supplementary Figure S10D),但其预测价值需要在独立数据集中进行验证。总之,我们的研究通过sc-eQTL定位鉴定了3,616个血液和702个肺癌特异性eGenes,并揭示了与CD8+ T细胞分化和ICI反应无应答相关的转录组学模块(CD8-brown)。eqtl对共表达网络的有限影响表明免疫转录组存在功能限制。通过将遗传变异与细胞毒性网络活性和临床结果联系起来,我们的分析提供了一个框架,以了解转移性非小细胞肺癌在ICI治疗下的全身免疫。未来的研究包括更大的队列和关键调控变异的实验验证,如影响PRF1和gzmb的那些,可以阐明因果机制并完善个性化的ICI策略。沈亨泰:数据管理;正式的分析;验证;调查;原创作品。Park Geun-Ho:数据管理;正式的分析;调查。朴雄阳:调查。李世勋:概念化;调查;原创作品。Murim Choi:概念化;调查;原创作品。作者声明没有利益冲突。 这项工作得到了韩国研究基金会(NRF-2021R1A2C3014067, NRF-RS-2023-00207857给Murim Choi, NRF-2020R1A2C3006535, NRF-RS-2025-00519956给Se-Hoon Lee),韩国健康技术研发项目(HR20C0025给Se-Hoon Lee)和三星医疗中心未来医学20*30项目(SMX1230041给Se-Hoon Lee)的部分资助。该研究调查了人体样本,并得到了三星首尔医院机构审查委员会(2018-04-048,2022-01-094)的许可。所有参与者在参加研究之前都经过了知情同意程序。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Cancer Communications
Cancer Communications Biochemistry, Genetics and Molecular Biology-Cancer Research
CiteScore
25.50
自引率
4.30%
发文量
153
审稿时长
4 weeks
期刊介绍: Cancer Communications is an open access, peer-reviewed online journal that encompasses basic, clinical, and translational cancer research. The journal welcomes submissions concerning clinical trials, epidemiology, molecular and cellular biology, and genetics.
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