• 中国科技论文统计源期刊(中国科技核心期刊)
  • 中国医药卫生核心期刊
  • 中国抗癌协会系列期刊

晚期肺腺癌瘤内趋化因子CCR1、CXCR6和CXCL9对免疫治疗的预测及预后作用分析

钟思敏, 张东东, 李扬秋, 吴一龙, 刘思阳, 金真伊

钟思敏, 张东东, 李扬秋, 吴一龙, 刘思阳, 金真伊. 晚期肺腺癌瘤内趋化因子CCR1、CXCR6和CXCL9对免疫治疗的预测及预后作用分析[J]. 循证医学, 2024, 24(2): 107-114. DOI: 10.12019/j.issn.1671-5144.202402013
引用本文: 钟思敏, 张东东, 李扬秋, 吴一龙, 刘思阳, 金真伊. 晚期肺腺癌瘤内趋化因子CCR1、CXCR6和CXCL9对免疫治疗的预测及预后作用分析[J]. 循证医学, 2024, 24(2): 107-114. DOI: 10.12019/j.issn.1671-5144.202402013
ZHONG Si-min, ZHANG Dong-dong, LI Yang-qiu, WU Yi-long, LIU Si-yang, JIN Zhen-yi. Prediction and Prognosis Analysis for Immunotherapy of Intra-tumoral Chemokines CCR1, CXCR6, and CXCL9 in Advanced Lung Adenocarcinoma[J]. Journal of Evidence-Based Medicine, 2024, 24(2): 107-114. DOI: 10.12019/j.issn.1671-5144.202402013
Citation: ZHONG Si-min, ZHANG Dong-dong, LI Yang-qiu, WU Yi-long, LIU Si-yang, JIN Zhen-yi. Prediction and Prognosis Analysis for Immunotherapy of Intra-tumoral Chemokines CCR1, CXCR6, and CXCL9 in Advanced Lung Adenocarcinoma[J]. Journal of Evidence-Based Medicine, 2024, 24(2): 107-114. DOI: 10.12019/j.issn.1671-5144.202402013

晚期肺腺癌瘤内趋化因子CCR1、CXCR6和CXCL9对免疫治疗的预测及预后作用分析

基金项目: 国家自然科学基金(82202997);广东省自然科学基金项目(2023A1515030271);广州市科技计划项目(202201010164);中国博士后科学基金第70批面上资助项目(2021M701422)。
详细信息
    作者简介:

    钟思敏(1998−),女,广东河源人,在读硕士研究生,主要从事肺癌研究

    通讯作者:

    刘思阳,E-mail:siyangliu2020@outlook.com

    金真伊,E-mail:jinzhenyijnu@163.com

  • 中图分类号: R734.2

Prediction and Prognosis Analysis for Immunotherapy of Intra-tumoral Chemokines CCR1, CXCR6, and CXCL9 in Advanced Lung Adenocarcinoma

  • 摘要:
    目的 

    构建能够预测晚期肺腺癌(lung adenocarcinoma,LUAD)患者免疫检查点阻断(immune checkpoint blockade, ICB)治疗疗效与预后的趋化因子模型。

    方法 

    从既往研究数据中回顾性收集确诊为晚期LUAD且经过ICB治疗的42例患者(训练队列),取得其ICB治疗前肿瘤组织转录组测序数据,通过生物信息学方法,筛选出显著影响ICB治疗疗效与患者生存预后的趋化因子,通过无进展生存期(progression-free disease,PFS)评价疗效,通过总生存期(overall survival,OS)评价患者的预后。从高通量基因表达数据库(Gene Expression Omnibus,GEO)中的数据集(GSE 135222)下载25例非小细胞肺癌(non-small cell lung cancer,NSCLC)转录组测序数据和相关的生存数据,将其作为验证队列。

    结果 

    通过单因素Cox回归分析,筛选出9个与晚期LUAD患者良好OS显著相关的趋化因子,其中CCR1、CXCR6和CXCL9高表达的患者明显具有更长的OS。基于这3个趋化因子的表达量构建风险模型,生存分析结果表明风险分数与患者的PFS和OS呈负相关,即低风险(risk score ≤ −1.33)的晚期LUAD患者在ICB治疗中明显获益更多。低风险组中位PFS为19.7个月对比高风险组(risk score > −1.33)2.9个月[95%可信区间(confidence interval,CI)为0.12~0.51,P < 0.001]。中位OS低风险组未达到对比高风险组6.0个月(95%CI 0.08~0.38,P < 0.001)。GEO数据集验证了该结果,中位PFS低风险(risk score ≤ −1.85)对比高风险(risk score > −1.85)为2.7个月对比1.2个月(95%CI 0.12~0.93,P = 0.009)。

    结论 

    CCR1、CXCR6和CXCL9高表达表明晚期LUAD患者疾病进展的风险较低,与患者良好的PFS和OS相关,可能为晚期LUAD患者的ICB辅助治疗提供新的方向。

    Abstract:
    Objective 

    Constructing a chemokine model to predict immune checkpoint blockade (ICB) efficacy and prognosis in advanced lung adenocarcinoma (LUAD).

    Methods 

    A total of 42 advanced LUAD patients (training cohort) who underwent ICB were retrospectively collected from the data of the previous study. We obtained transcriptome sequencing data from tumor tissues before ICB treatment. Screening for chemokines that significantly affect the efficacy of ICB treatment and the survival prognosis of patients with advanced LUAD by bioinformatics methods, and the efficacy was evaluated by progression-free disease (PFS), and the prognosis was evaluated by overall survival (OS). Transcriptome sequencing and survival-related data of 25 cases of non-small cell lung cancer (NSCLC) were downloaded from the Gene Expression Omnibus (GEO) database dataset (GSE 135222) and identified as a validation cohort.

    Results 

    Through univariate Cox regression analysis, nine chemokines significantly associated with favorable OS in advanced LUAD patients were screened, among which patients with high expression of CCR1, CXCR6, and CXCL9 had significantly longer OS. A risk model based on the expression of these three chemokines could be constructed. Survival analysis results show that risk score was negatively correlated with patients’ PFS and OS, which means advanced LUAD patients with low risk (risk score ≤ −1.33) could benefit more from ICB treatment. The median PFS in the low-risk group was 19.7 months, whereas in the high-risk group (risk score > −1.33) was 2.9 months [95% confidence interval (CI) 0.12~0.51, P < 0.001], and the median OS in the high-risk group was 6.0 months, while it was not reached in the low-risk group (95%CI 0.08~0.38, P < 0.001). The results from the GEO dataset confirmed the association between the risk score and PFS. Patients were classified as low-risk based on the risk score (risk score ≤ −1.85) were associated with a longer median PFS of 2.7 months, whereas the high-risk group (risk score > −1.85) had a shorter median PFS of 1.2 months (95%CI 0.12~0.93, P = 0.009).

    Conclusions 

    The high expression of CCR1, CXCR6, and CXCL9 indicates that patients with advanced LUAD have a low risk of disease progression, which is associated with favorable PFS and OS, and may provide a novel perspective for the adjuvant setting of ICB therapy for advanced LUAD patients.

  • 肺癌是全世界癌症死亡的主要原因,其常见的病理亚型是肺腺癌(lung adenocarcinoma,LUAD),约占所有肺癌病例的40% [1-2]。过去几十年,免疫检查点阻断(immune checkpoint blockade,ICB)治疗方式的发展,显著改善了晚期LUAD患者的预后[3-4]。然而,仍然存在一些LUAD患者无法从免疫治疗中获得显著的效益[5]。因此,寻找更为有效的免疫治疗预测因子以及开发合理的药物联合治疗成为当务之急[6]。随着测序技术的进步,RNA测序通过探讨肿瘤微环境(tumor microenvironment,TME)中复杂的肿瘤与免疫细胞的相互作用,成为预测各类癌症患者对ICB反应的有力工具[7]

    最近的研究表明,免疫细胞向TME的浸润受到趋化因子和趋化因子受体相互作用的调节,CC基序和CXC基序趋化因子表达可能通过塑造浸润性免疫细胞群来影响肿瘤进展[8-10]。例如,在恶性黑色素瘤向淋巴结转移的过程中,自然杀伤细胞在淋巴结中的富集为TME中CXC基序趋化因子配体8(C-X-C motif chemokine ligand 8,CXCL8)的释放所介导[11],而CXC基序趋化因子配体9(C-X-C motif chemokine ligand 9, CXCL9)和CXC基序趋化因子配体10(C-X-C motif chemokine ligand 10, CXCL10)则是募集效应CD8+ T细胞到黑色素瘤肿瘤微环境中的关键趋化因子[12]。CC基序趋化因子配体3(C-C motif chemokine ligand 3,CCL3)同样可以通过募集T淋巴细胞发挥有效的抗肿瘤作用[13]。然而,Facciabene等人则发现CC基序趋化因子配体28(C-C motif chemokine ligand 3,CCL28)在卵巢癌中因缺氧表达上调,并且与肿瘤的生长直接相关,这可能与CCL28与其受体结合将调节性T细胞募集到肿瘤部位有关[14]

    综上所述,趋化因子-趋化因子受体相互作用可能在肿瘤免疫微环境塑造过程中起到关键作用,这可能影响患者的ICB治疗效果,从而成为其预后因子。因此,将趋化因子-趋化因子受体信号通路作为靶标,可能成为增强ICB治疗疗效的补充策略[15]。目前,影响晚期LUAD患者ICB疗效和预后的趋化因子类别及其预测效能如何尚不明确。因此,本研究回顾性分析了接受ICB治疗的42例晚期LUAD和GEO数据库中(GSE 135222 )数据集25例NSCLC患者的转录组测序数据和生存相关数据,寻找有效的预测标志物。

    回顾性收集42例接受ICB治疗且有RNA测序数据的患者,将其作为训练队列[7]。患者的基线特征如表1所示。入组的患者年龄范围为45~74岁,中位年龄为61岁。男性37例(88.1%),女性5例(11.9%)。具有吸烟史的患者29例(69.0%),从未吸烟的患者13例(31.0%)。东部肿瘤合作组健康状态评分(Eastern Cooperative Oncology Group performance status score,EGOC PS score)0~1分患者37例(88.1%),2~3分患者5例(11.9%)。伴随脑转移患者9例(21.4%),无脑转移患者33例(78.6%)。一线治疗患者16例(38.1%),非一线治疗患者26例(61.9%)。经过免疫治疗后部分缓解(partial response,PR)的患者12例(28.6%),疾病稳定(stable disease,SD)患者17例(40.5%),疾病进展(progression disease,PD)患者13例(30.9%)。

    表  1  42例晚期LUAD患者基线特征
    Table  1.  Clinical and pathological characteristics of42 advanced LUAD patients
    Characteristics n =42
    Age, median (range) 61(45~74)
    Sex, n (%)
     Male 37 (88.1%)
     Female 5 (11.9%)
    Smoking history, n (%)
     Never 13 (31.0%)
     Ever 29 (69.0%)
    ECOG PS score, n (%)
     0~1 37 (88.1%)
     2~3 5 (11.9%)
    Stage, n (%)
     ⅢB 2 (4.8%)
     ⅣA 21 (50.0%)
     ⅣB 19 (45.2%)
    Line of immune checkpoint blockade, n (%)
     1st 16 (38.1%)
     2nd 17 (40.5%)
     3rd 6 (14.3%)
     4th and beyond 3 (7.1%)
    With brain metastases, n (%)
     No 33 (78.6%)
     Yes 9 (21.4%)
    Treatment response, n (%)
     PR 12 (28.6%)
     SD 17 (40.5%)
     PD 13 (30.9%)
    下载: 导出CSV 
    | 显示表格

    从GEO数据库(https://www.ncbi.nlm.nih.gov/)的数据集(GSE 135222 )下载25例NSCLC患者的RNA测序数据以及生存相关数据,并将其作为验证队列。患者年龄范围为44~73岁,中位年龄为62岁。男性患者20例(80.0%),女性患者5例(20.0%)。

    采用IBM SPSS Statistics 27.0(version 27.0)软件进行单因素和多因素Cox回归分析。R语言(version 4.3.1)建立最优预测模型。利用X-tile(version 3.6.1)软件获得risk score的最佳截断值。通过GraphPad Prism(version 8.4.2)运用Kaplan-Meier生存曲线进行生存分析,比较高风险组与低风险组患者的OS和PFS;以及受试者工作特征曲线(receiver operating characteristic curve,ROC)分析计算曲线下面积(area under curve,AUC)。风险分数计算方式为各因素的影响系数与基因表达量乘积之和。

    总共在56个CC基序和CXC基序的趋化因子中,通过单因素Cox回归分析得到9个与患者良好OS相关的趋化因子(P < 0.1),其中7个趋化因子具有显著统计学意义(P < 0.05,如图1A所示)。以此为基础,进一步分析建立最佳的预测模型,如图1B所示,趋化因子受体1(C-C motif chemokine receptor 1,CCR1),趋化因子受体6(C-X-C motif chemokine receptor 6,CXCR6),CXCL9的组合为预测患者OS的最佳模型。将模型的3个趋化因子纳入多因素Cox回归分析,获取各个趋化因子的影响系数,发现CXCR6对患者OS预测的贡献最大(P < 0.05)。根据多因素Cox回归分析所得系数,计算各患者的风险分数:风险分数=−0.11×(CCR1表达量)−0.14×(CXCR6表达量)−0.01×(CXCL9表达量),如图1C所示,推测3个趋化因子组合得到的风险模型具有对晚期LUAD患者进行风险分层的潜能,即将患者区分为高风险人群和低风险人群。

    图  1  建立预测晚期LUAD患者ICB疗效与预后的风险模型
    注:A. 9个影响晚期LUAD患者生存的趋化因子;B. 建立最佳的晚期LUAD患者ICB治疗疗效与预后的预测模型,分别为CCR1,CXCR6,CXCL9;C. 多因素Cox回归分析计算3个趋化因子的影响系数。
    Figure  1.  Establishing a risk model for predicting ICB efficacy and prognosis in advanced LUAD patients
    Note: A. 9 chemokines that affect survival in advanced LUAD patients; B. The optimal prediction model for ICB efficacy and prognosis of advanced LUAD patients were established, which were CCR1, CXCR6, and CXCL9 respectively; C. Multivariate Cox regression analysis was used to calculate the influence coefficients of the three chemokines.

    根据各患者的风险分数不同,可通过X-Tile软件将患者分为高风险组和低风险组。共有19例患者属于低风险组,23例患者归为高风险组。综合Kaplan-Meier生存分析和多因素Cox回归分析,表明风险分数与PFS和OS呈负相关,即与高风险组的患者相比,低风险组患者的PFS明显更长,低风险组对比高风险组中位PFS为19.7个月对比2.9个月[95%可信区间(confidence interval,CI)为0.12~0.51,P < 0.001](如图2A左图所示)。同样地,高风险患者的OS更短,低风险组中位OS未达到对比高风险组6.0个月(95%CI 0.08~0.38,P < 0.001)(如图2A右图所示)。ROC曲线分析(如图2B所示)发现风险模型具有良好的预测效能(AUCPFS = 0.88,95%CI 0.78~0.99,P < 0.001;AUCOS = 0.84,95%CI 0.68~0.99,P < 0.001),这表明该模型能够较好地预测晚期LUAD患者在ICB治疗中良好的疗效和预后。为确定3个趋化因子组合建立的风险模型是否可以独立预测晚期LUAD患者的ICB疗效和预后,我们进行了单因素和多因素Cox回归分析。将患者的性别、年龄、吸烟史、PS评分、临床分期、是否脑转移、治疗线数和风险分数纳入Cox回归模型进行生存分析,结果表明基于趋化因子建立的风险分数是晚期LUAD患者PFS和OS的独立预后预测因素(如表2所示)。

    图  2  基于风险模型的生存分析
    注:A. 晚期LUAD患者风险分层生存分析,左图为PFS,右图为OS;B. 预测模型对晚期LUAD患者ICB疗效和预后预测的概率分析;C. CCR1,CXCR6,CXCL9单独高表达提高晚期LUAD患者ICB的临床效益。
    Figure  2.  Survival analysis based on risk model
    Note: A. Risk stratified survival analysis on PFS (left panel) and OS (right panel) of patients with advanced LUAD; B. Probabilistic analysis of the prediction model for advanced LUAD patients’ efficacy and prognosis; C. The high expression of CCR1, CXCR6 and CXCL9 alone can improve the clinical benefit of advanced LUAD patients with ICB.
    表  2  晚期LUAD患者单因素-多因素Cox回归分析
    Table  2.  Univariate and multivariate Cox regression analysis in advanced LUAD patients
    Variables PFS OS
    Univariate COX Multivariate COX Univariate COX Multivariate COX
    HR (95%CI) P value HR (95%CI) P value HR (95%CI) P value HR (95%CI) P value
    Risk score
     Low risk Reference
     High risk 5.05 (2.19, 11.67) <0.001 5.60 (2.24, 13.95) <0.001 7.41 (2.48, 22.12) <0.001 9.54 (2.82, 32.25) <0.001
    Sex
     Female Reference
     Male 0.98 (0.34, 2.82) 0.971 2.73 (0.62, 12.01) 0.185 0.53 (0.15, 1.87) 0.323 0.87 (0.14, 5.32) 0.877
    Age
     <60 years Reference
     ≥ 60 years 0.45 (0.23, 0.91) 0.026 0.51 (0.21, 1.26) 0.147 0.52 (0.23, 1.14) 0.101 0.65 (0.24, 1.72) 0.382
    Smoking history
     Never Reference
     Ever/current 0.97 (0.46, 2.05) 0.931 1.45 (0.58, 3.64) 0.424 0.68 (0.29, 1.58) 0.369 1.32 (0.44, 3.95) 0.625
    ECOG PS score
     ≤ 1 Reference
     >1 2.34 (0.80, 6.81) 0.119 5.16 (1.29, 20.59) 0.02 6.30 (1.92, 20.69) 0.002 10.72 (2.24, 51.23) 0.003
    Clinical stages
     Ⅲ Reference
     Ⅳ 0.31 (0.70, 1.37) 0.122 0.65 (0.13, 3.34) 0.605 0.56 (0.13, 2.43) 0.443 1.67 (0.27, 10.45) 0.583
    With brain metastasis
     No Reference
     Yes 0.63 (0.26, 1.55) 0.316 0.60 (0.21, 1.71) 0.342 0.73 (0.25, 2.16) 0.574 0.33 (0.08, 1.28) 0.108
    Lines of therapy
     First-line Reference
     Non-first-line 1.01 (0.50, 2.04) 0.981 1.41 (0.57, 3.49) 0.461 0.95 (0.42, 2.11) 0.891 1.05 (0.38, 2.93) 0.929
    注:单因素和多因素Cox回归显示,风险评分P< 0.05在训练队列中具有统计学意义。HR:风险比。
    Note: Univariate and multivariate Cox regression shown that P values <0.05 in risk score were statistically significant in the training cohort. HR: hazard ratio.
    下载: 导出CSV 
    | 显示表格

    由于趋化因子表达对ICB疗效和预后的重要性,我们进一步分别研究了CCR1、CXCR6和CXCL9与PFS和OS的关系。根据CCR1,CXCR6,CXCL9的表达量获取最佳截断值,将晚期LUAD患者分别分为趋化因子高表达和低表达组。在CCR1的生存分析中,发现CCR1表达量与PFS和OS正相关,即晚期LUAD患者的CCR1高表达时,中位PFS为9.5个月显著高于低表达患者2.8个月(95%CI 0.21~0.86,P = 0.013),中位OS高表达患者未达到对比低表达患者为9.5个月(95%CI 0.14~0.70,P = 0.006)。当对CXCR6进行分析时,结果表明CXCR6高表达改善了晚期LUAD患者的临床获益效果,中位PFS达到19.7个月对比低表达组2.7个月(95%CI 0.10~0.41,P < 0.001),中位OS未达到对比低表达组为5.6个月(95%CI 0.08~0.41,P < 0.001)。在CXCL9的生存分析中观察到了类似的影响作用,晚期LUAD患者高表达CXCL9,经ICB治疗后PFS和OS均显著延长,中位PFS为9.5个月对比低表达患者2.4个月(95%CI 0.09~0.52,P < 0.001),中位OS高表达对比低表达患者为25.0个月对比4.3个月(95%CI 0.11~0.64,P < 0.001)。因此,综上结果表明3个趋化因子独立高表达均为有益于延长晚期LUAD患者PFS和OS的因素(P < 0.05),该结果与风险模型的预测结果一致,如图2C所示。

    为进一步验证风险模型的预测效能,我们从GEO数据库中的数据集(GSE 135222 )下载接受ICB治疗的25例NSCLC患者。通过与训练队列相同的计算方式,分别计算每位患者的风险分数,随后利用X-Tile获取最佳截断值,共有低风险患者15例,高风险患者10例。生存分析的结果表明低风险组的患者比高风险组患者明显能从ICB治疗中获得较好的疗效,低风险组中位PFS对比高风险组为2.7个月对比1.2个月(95%CI 0.12~0.93,P = 0.009)(如图3A所示)。此外,CCR1,CXCR6,CXCL9独立高表达,均与改善NSCLC患者的PFS显著相关(P < 0.05)(如图3B所示)。

    图  3  验证风险模型适用性
    注:A. NSCLC患者PFS生存分析;B. CCR1,CXCR6,CXCL9高表达有利于改善NSCLC患者的ICB治疗效果。
    Figure  3.  Validation of risk model applicability
    Note: A. PFS survival analysis of patients with NSCLC; B. The high expression of CCR1, CXCR6 and CXCL9 was conducive to improving the ICB therapeutic effect of NSCLC patients.

    研究表明免疫细胞在TME中的浸润是癌症预后的重要因素,趋化因子在诱导激活和抑制免疫细胞类型的迁移过程中起着至关重要的作用[16-18]。虽然免疫细胞向肿瘤组织的迁移因为实体瘤的异位性和异质性而难以预测,但了解实体瘤的趋化环境和识别调节免疫细胞进入实体瘤的趋化因子,对于改善当前的ICB治疗策略尤为重要[19-20]

    对于CCR1,Liu等人发现其在LUAD患者肿瘤组织中表达较CC基序的其他趋化因子受体高,且其表达量与CD8+ T细胞浸润肿瘤组织呈正相关,与患者的临床结果显著相关[21]。Xiong等研究人员在关于黑色素瘤的研究中也发现CCR1与CD8+ T细胞浸润正相关,并且CCR1高表达提示患者预后良好[22]。综上结果,CCR1可能通过影响免疫状态参与肿瘤的进展。而在本研究中,同样发现CCR1高表达具有延长晚期LUAD的PFS和OS的潜力,因此我们推测CCR1可能通过诱导免疫细胞进入TME以消除肿瘤细胞来影响患者的预后。

    其次是关于CXCR6,则有先前关于肺癌的研究报道CXCR6蛋白在组织驻留记忆CD8+ T细胞上表达较高,利于T细胞驻留在肿瘤组织,使免疫细胞发生抗肿瘤作用[23]。另外,在胃肠道的研究结果中则发现CXCR6仅在瘤内的CD8+ T细胞上高表达,这类细胞具有抗肿瘤抗原特异性,可以增强程序性死亡受体1(programmed cell death protein 1, PD-1)的阻断作用,进而延缓患者的肿瘤进展[24]。这些结果表明,T细胞浸润肿瘤组织也可能受到CXCR6与其配体相互作用的调节。基于本研究的结果,推测可能是高表达CXCR6的CD8+ T细胞被招募至肿瘤部位,形成炎症型的TME,进而改善患者的生存预后。

    在一项重要的研究中,Hoch等人发现在免疫热肿瘤中CXCL9表达频数较高,而冷肿瘤中几乎没有趋化因子表达,证明了肿瘤组织表达CXCL9有助于“热”肿瘤的形成[25]。同样地,Niño等也证明了在T细胞丰富的样品中,CXCL9高表达的TME构成了炎症和抗肿瘤反应性的特点[26]。与之契合的是,本研究同样发现可能是由于CXCL9高表达助力患者形成“热”肿瘤,进而改善患者的ICB疗效和预后。

    目前尚无研究综合讨论CCR1,CXCR6以及CXCL9与晚期LUAD患者ICB疗效和生存预后的关系。本研究综合上述3个趋化因子构建风险模型,发现低风险组患者可能更大限度地从ICB治疗中的获益,并且该结果均得到GEO数据库中NSCLC患者的数据验证。

    综上所述,本研究构建的风险分数预测模型是有效预测晚期LUAD患者接受ICB治疗和判断生存预后的良好预测因子,CCR1,CXCR6,CXCL9可能通过与各自的配体相互作用等多种方式,将不同类型的免疫细胞募集到肿瘤部位,塑造炎症和抗肿瘤反应性的TME,使晚期LUAD患者对ICB治疗产生应答,最终使其ICB治疗效果与预后得以改善。

  • 图  1   建立预测晚期LUAD患者ICB疗效与预后的风险模型

    注:A. 9个影响晚期LUAD患者生存的趋化因子;B. 建立最佳的晚期LUAD患者ICB治疗疗效与预后的预测模型,分别为CCR1,CXCR6,CXCL9;C. 多因素Cox回归分析计算3个趋化因子的影响系数。

    Figure  1.   Establishing a risk model for predicting ICB efficacy and prognosis in advanced LUAD patients

    Note: A. 9 chemokines that affect survival in advanced LUAD patients; B. The optimal prediction model for ICB efficacy and prognosis of advanced LUAD patients were established, which were CCR1, CXCR6, and CXCL9 respectively; C. Multivariate Cox regression analysis was used to calculate the influence coefficients of the three chemokines.

    图  2   基于风险模型的生存分析

    注:A. 晚期LUAD患者风险分层生存分析,左图为PFS,右图为OS;B. 预测模型对晚期LUAD患者ICB疗效和预后预测的概率分析;C. CCR1,CXCR6,CXCL9单独高表达提高晚期LUAD患者ICB的临床效益。

    Figure  2.   Survival analysis based on risk model

    Note: A. Risk stratified survival analysis on PFS (left panel) and OS (right panel) of patients with advanced LUAD; B. Probabilistic analysis of the prediction model for advanced LUAD patients’ efficacy and prognosis; C. The high expression of CCR1, CXCR6 and CXCL9 alone can improve the clinical benefit of advanced LUAD patients with ICB.

    图  3   验证风险模型适用性

    注:A. NSCLC患者PFS生存分析;B. CCR1,CXCR6,CXCL9高表达有利于改善NSCLC患者的ICB治疗效果。

    Figure  3.   Validation of risk model applicability

    Note: A. PFS survival analysis of patients with NSCLC; B. The high expression of CCR1, CXCR6 and CXCL9 was conducive to improving the ICB therapeutic effect of NSCLC patients.

    表  1   42例晚期LUAD患者基线特征

    Table  1   Clinical and pathological characteristics of42 advanced LUAD patients

    Characteristics n =42
    Age, median (range) 61(45~74)
    Sex, n (%)
     Male 37 (88.1%)
     Female 5 (11.9%)
    Smoking history, n (%)
     Never 13 (31.0%)
     Ever 29 (69.0%)
    ECOG PS score, n (%)
     0~1 37 (88.1%)
     2~3 5 (11.9%)
    Stage, n (%)
     ⅢB 2 (4.8%)
     ⅣA 21 (50.0%)
     ⅣB 19 (45.2%)
    Line of immune checkpoint blockade, n (%)
     1st 16 (38.1%)
     2nd 17 (40.5%)
     3rd 6 (14.3%)
     4th and beyond 3 (7.1%)
    With brain metastases, n (%)
     No 33 (78.6%)
     Yes 9 (21.4%)
    Treatment response, n (%)
     PR 12 (28.6%)
     SD 17 (40.5%)
     PD 13 (30.9%)
    下载: 导出CSV

    表  2   晚期LUAD患者单因素-多因素Cox回归分析

    Table  2   Univariate and multivariate Cox regression analysis in advanced LUAD patients

    Variables PFS OS
    Univariate COX Multivariate COX Univariate COX Multivariate COX
    HR (95%CI) P value HR (95%CI) P value HR (95%CI) P value HR (95%CI) P value
    Risk score
     Low risk Reference
     High risk 5.05 (2.19, 11.67) <0.001 5.60 (2.24, 13.95) <0.001 7.41 (2.48, 22.12) <0.001 9.54 (2.82, 32.25) <0.001
    Sex
     Female Reference
     Male 0.98 (0.34, 2.82) 0.971 2.73 (0.62, 12.01) 0.185 0.53 (0.15, 1.87) 0.323 0.87 (0.14, 5.32) 0.877
    Age
     <60 years Reference
     ≥ 60 years 0.45 (0.23, 0.91) 0.026 0.51 (0.21, 1.26) 0.147 0.52 (0.23, 1.14) 0.101 0.65 (0.24, 1.72) 0.382
    Smoking history
     Never Reference
     Ever/current 0.97 (0.46, 2.05) 0.931 1.45 (0.58, 3.64) 0.424 0.68 (0.29, 1.58) 0.369 1.32 (0.44, 3.95) 0.625
    ECOG PS score
     ≤ 1 Reference
     >1 2.34 (0.80, 6.81) 0.119 5.16 (1.29, 20.59) 0.02 6.30 (1.92, 20.69) 0.002 10.72 (2.24, 51.23) 0.003
    Clinical stages
     Ⅲ Reference
     Ⅳ 0.31 (0.70, 1.37) 0.122 0.65 (0.13, 3.34) 0.605 0.56 (0.13, 2.43) 0.443 1.67 (0.27, 10.45) 0.583
    With brain metastasis
     No Reference
     Yes 0.63 (0.26, 1.55) 0.316 0.60 (0.21, 1.71) 0.342 0.73 (0.25, 2.16) 0.574 0.33 (0.08, 1.28) 0.108
    Lines of therapy
     First-line Reference
     Non-first-line 1.01 (0.50, 2.04) 0.981 1.41 (0.57, 3.49) 0.461 0.95 (0.42, 2.11) 0.891 1.05 (0.38, 2.93) 0.929
    注:单因素和多因素Cox回归显示,风险评分P< 0.05在训练队列中具有统计学意义。HR:风险比。
    Note: Univariate and multivariate Cox regression shown that P values <0.05 in risk score were statistically significant in the training cohort. HR: hazard ratio.
    下载: 导出CSV
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    1. 卜亚静,肖敏,秦德华,时昌立,刘松雅,孟力. 外周血CX3CR1表达与弥漫大B细胞淋巴瘤CAR-T治疗后复发的关系. 交通医学. 2025(02): 111-116 . 百度学术

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  • 收稿日期:  2024-02-22
  • 网络出版日期:  2024-07-12
  • 刊出日期:  2024-04-27

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