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通讯作者:

冯振卿,E-mail:fengzhenqing@njmu.edu.cn

中图分类号:R734.2

文献标识码:A

文章编号:1007-4368(2023)06-864-07

DOI:10.7655/NYDXBNS20230618

参考文献 1
GANDHI L,RODRÍGUEZ ⁃ ABREU D,GADGEEL S,et al.Pembrolizumab plus chemotherapy in metastatic non ⁃ small⁃cell lung cancer[J].N Engl J Med,2018,378(22):2078-2092
参考文献 2
SIEGEL R L,MILLER K D,JEMAL A.Cancer statistics,2019[J].CA A Cancer J Clin,2019,69(1):7-34
参考文献 3
CAO M M,CHEN W Q.Epidemiology of lung cancer in China[J].Thorac Cancer,2019,10(1):3-7
参考文献 4
TOPALIAN S L,TAUBE J M,ANDERS R A,et al.Mecha⁃ nism ⁃ driven biomarkers to guide immune checkpoint blockade in cancer therapy[J].Nat Rev Cancer,2016,16(5):275-287
参考文献 5
RIBAS A,WOLCHOK J D.Cancer immunotherapy using checkpoint blockade[J].Science,2018,359(6382):1350-1355
参考文献 6
王梦玮,徐天蔚,王朝霞.PD⁃1/PD⁃L1免疫检查点抑制剂在肺癌临床研究中的进展[J].南京医科大学学报(自然科学版),2021,41(7):1084-1094
参考文献 7
HERBST R S,BAAS P,KIM D W,et al.Pembrolizumab versus docetaxel for previously treated,PD ⁃ L1⁃ positive,advanced non⁃small⁃cell lung cancer(KEYNOTE⁃010):a randomised controlled trial [J].Lancet,2016,387(10027):1540-1550
参考文献 8
SODA M,CHOI Y L,ENOMOTO M,et al.Identification of the transforming EML4⁃ALK fusion gene in non⁃small⁃ cell lung cancer[J].Nature,2007,448(7153):561-566
参考文献 9
PAEZ J G,JÄNNE P A,LEE J C,et al.EGFR mutations in lung cancer:correlation with clinical response to gefi⁃ tinib therapy[J].Science,2004,304(5676):1497-1500
参考文献 10
TSUI D W Y,MURTAZA M,WONG A S C,et al.Dyna⁃ mics of multiple resistance mechanisms in plasma DNA during EGFR ⁃ targeted therapies in non ⁃ small cell lung cancer[J].EMBO Mol Med,2018,10(6):7945
参考文献 11
SADE ⁃FELDMAN M,YIZHAK K,BJORGAARD S L,et al.Defining T cell states associated with response to checkpoint immunotherapy in melanoma[J].Cell,2019,176(1/2):404
参考文献 12
LAVIN Y,KOBAYASHI S,LEADER A,et al.Innate im⁃ mune landscape in early lung adenocarcinoma by paired single⁃cell analyses[J].Cell,2017,169(4):750-765
参考文献 13
GUO X Y,ZHANG Y Y,ZHENG L T,et al.Global chara⁃ cterization of T cells in non⁃small⁃cell lung cancer by sin⁃ gle⁃cell sequencing[J].Nat Med,2018,24(7):978-985
参考文献 14
ZHENG C H,ZHENG L T,YOO J K,et al.Landscape of infiltrating T cells in liver cancer revealed by single ⁃cell sequencing[J].Cell,2017,169(7):1342-1356
参考文献 15
ZHANG Q M,HE Y,LUO N,et al.Landscape and dyna⁃ mics of single immune cells in hepatocellular carcinoma [J].Cell,2019,179(4):829-845
参考文献 16
YOST K E,SATPATHY A T,WELLS D K,et al.Clonal replacement of tumor ⁃ specific T cells following PD ⁃ 1 blockade[J].Nat Med,2019,25(8):1251-1259
参考文献 17
ZHANG L,YU X,ZHENG L T,et al.Lineage tracking re⁃ veals dynamic relationships of T cells in colorectal cancer [J].Nature,2018,564(7735):268-272
参考文献 18
PENTER L,DIETZE K,RITTER J,et al.Localization⁃as⁃ sociated immune phenotypes of clonally expanded tumor⁃ infiltrating T cells and distribution of their target antigens in rectal cancer[J].OncoImmunology,2019,8(6):e1586409
参考文献 19
SAVAS P,VIRASSAMY B,YE C Z,et al.Single⁃cell pro⁃ filing of breast cancer T cells reveals a tissue ⁃ resident memory subset associated with improved prognosis[J].Nat Med,2018,24(7):986-993
参考文献 20
NEFTEL C,LAFFY J,FILBIN M G,et al.An integrative model of cellular states,plasticity,and genetics for glio⁃ blastoma[J].Cell,2019,178(4):835-849
参考文献 21
SON B,LEE S,YOUN H,et al.The role of tumor microen⁃ vironment in therapeutic resistance[J].Oncotarget,2017,8(3):3933-3945
参考文献 22
BALKWILL F R,CAPASSO M,HAGEMANN T.The tu⁃ mor microenvironment at a glance[J].J Cell Sci,2012,125(23):5591-5596
参考文献 23
WU F Y,FAN J,HE Y Y,et al.Single⁃cell profiling of tu⁃ mor heterogeneity and the microenvironment in advanced non ⁃ small cell lung cancer[J].Nat Commun,2021,12(1):2540
参考文献 24
KARGL J,BUSCH S E,YANG G H Y,et al.Neutrophils dominate the immune cell composition in non ⁃ small cell lung cancer[J].Nat Commun,2017,8:14381
参考文献 25
MOLLAOGLU G,JONES A,WAIT S J,et al.The lineage⁃ defining transcription factors SOX2 and NKX2 ⁃ 1 deter⁃ mine lung cancer cell fate and shape the tumor immune microenvironment[J].Immunity,2018,49(4):764-779
参考文献 26
WOLF Y,BARTOK O,PATKAR S,et al.UVB ⁃induced tumor heterogeneity diminishes immune response in mela⁃ noma[J].Cell,2019,179(1):219-235
参考文献 27
KARGL J,ZHU X D,ZHANG H J,et al.Neutrophil con⁃ tent predicts lymphocyte depletion and anti ⁃ PD1 treat⁃ ment failure in NSCLC [J].JCI Insight,2019,4(24):e130850
参考文献 28
LAMBRECHTS D,WAUTERS E,BOECKX B,et al.Phe⁃ notype molding of stromal cells in the lung tumor microen⁃ vironment[J].Nat Med,2018,24(8):1277-1289
参考文献 29
CHEN X,WAN J,LIU J K,et al.Increased IL⁃17⁃produc⁃ ing cells correlate with poor survival and lymphangiogene⁃ sis in NSCLC patients[J].Lung Cancer,2010,69(3):348-354
参考文献 30
WELSH T J,GREEN R H,RICHARDSON D,et al.Mac⁃ rophage and mast⁃cell invasion of tumor cell islets confers a marked survival advantage in non⁃small⁃cell lung cancer [J].J Clin Oncol,2005,23(35):8959-8967
参考文献 31
SHARMA P,ALLISON J P.Immune checkpoint targeting in cancer therapy:toward combination strategies with cu⁃ rative potential[J].Cell,2015,161(2):205-214
参考文献 32
严晓娣,史国振,毛旭华.高通量测序技术检测非小细胞肺癌相关驱动基因的突变[J].南京医科大学学报(自然科学版),2021,41(2):193-197
参考文献 33
BLAKELY C M,WATKINS T B K,WU W,et al.Evolu⁃ tion and clinical impact of co ⁃ occurring genetic altera⁃ tions in advanced ⁃ stage EGFR ⁃mutant lung cancers[J].Nat Genet,2017,49(12):1693-1704
参考文献 34
TAMMELA T,SANCHEZ⁃RIVERA F J,CETINBAS N M,et al.A Wnt ⁃ producing niche drives proliferative poten⁃ tial and progression in lung adenocarcinoma[J].Nature,2017,545(7654):355-359
参考文献 35
KRISHNAMURTHY N,KURZROCK R.Targeting the Wnt/beta ⁃ catenin pathway in cancer:update on effectors and inhibitors[J].Cancer Treat Rev,2018,62:50-60
参考文献 36
AISSA A F,ISLAM A B M M K,ARISS M M,et al.Single ⁃ cell transcriptional changes associated with drug toler⁃ ance and response to combination therapies in cancer[J].Nat Commun,2021,12:1628
参考文献 37
LI C,XUE V W,WANG Q M,et al.The mincle/syk/nf⁃κb signaling circuit is essential for maintaining the protumoral activities of tumor ⁃ associated macrophages[J].Cancer Immunol Res,2020,8(8):1004-1017
参考文献 38
SINJAB A,HAN G C,TREEKITKARNMONGKOL W,et al.Resolving the spatial and cellular architecture of lung adenocarcinoma by multiregion single ⁃ cell sequencing [J].Cancer Discov,2021,11(10):2506-2523
参考文献 39
DUARTE I F,ROCHA C M,GIL A M.Metabolic profil⁃ ing of biofluids:potential in lung cancer screening and di⁃agnosis[J].Expert Rev Mol Diagn,2013,13(7):737-748
参考文献 40
WANG G,QIU M,XING X,et al.Lung cancer scrna⁃seq and lipidomics reveal aberrant lipid metabolism for early⁃ stage diagnosis[J].Sci Transl Med,2022,14(630):eabk2756
参考文献 41
YANG L,YAN X W,CHEN J,et al.Hexokinase 2 dis⁃ cerns a novel circulating tumor cell population associated with poor prognosis in lung cancer patients[J].Proc Natl Acad Sci U S A,2021,118(11):e2012228118
参考文献 42
HAN K,WANG J K,QIAN K,et al.Construction of a prognostic model for non⁃small⁃cell lung cancer based on ferroptosis ⁃ related genes[J].Biosci Rep,2021,41(5):BSR20210527
参考文献 43
KASHIMA Y,SHIBAHARA D,SUZUKI A,et al.Single⁃ cell analyses reveal diverse mechanisms of resistance to EGFR tyrosine kinase inhibitors in lung cancer[J].Can⁃ cer Res,2021,81(18):4835-4848
参考文献 44
MA S Q,ZHANG L,REN Y,et al.Epiregulin confers EGFR ⁃ TKI resistance via EGFR/ErbB2 heterodimer in non⁃small cell lung cancer[J].Oncogene,2021,40(14):2596-2609
参考文献 45
BI G S,LIANG J Q,ZHAO M N,et al.miR ⁃ 6077 pro⁃motes cisplatin/pemetrexed resistance in lung adenocarci⁃ noma via CDKN1A/cell cycle arrest and KEAP1/ferroptosis pathways[J].Mol Ther Nucleic Acids,2022,28:366-386
参考文献 46
MAYNARD A,MCCOACH C E,ROTOW J K,et al.Therapy⁃ induced evolution of human lung cancer revealed by sin⁃ gle⁃cell RNA sequencing[J].Cell,2020,182(5):1232-1251
参考文献 47
WU Q,WANG L,WEI H G,et al.Integration of multiple key molecules in lung adenocarcinoma identifies prognos⁃ tic and immunotherapeutic relevant gene signatures[J].Int Immunopharmacol,2020,83:106477
参考文献 48
ZHAO M N,CHEN Z C,ZHENG Y S,et al.Identification of cancer stem cell⁃related biomarkers in lung adenocarci⁃ noma by stemness index and weighted correlation network analysis[J].J Cancer Res Clin Oncol,2020,146(6):1463-1472
参考文献 49
ZENG H,JI J R,SONG X D,et al.Stemness related genes revealed by network analysis associated with tumor im⁃ mune microenvironment and the clinical outcome in lung adenocarcinoma[J].Front Genet,2020,11:549213
目录contents

    摘要

    肺癌是世界范围内死亡率最高的癌症,其中以非小细胞肺癌(non-small cell lung cancer,NSCLC)最为常见。以CAR-T 细胞治疗、免疫检查点阻断为代表的肿瘤免疫治疗,成为肺癌免疫治疗研究领域的前沿热点,但由于肿瘤免疫抑制性微环境、肿瘤特异性抗原的缺乏以及耐药性等不良因素的影响,其临床治疗效果仍不理想,亟需探究肺癌相关新的分子靶点和分子机制。单细胞RNA测序(single-cell RNA sequencing,scRNA-seq)技术是在单细胞水平对转录组进行高通量测序的一项新技术,可以研究单个细胞内的基因表达情况,从而了解细胞的功能和状态。利用该技术有望深入了解肺癌的发生发展机制,发现新的肺癌治疗靶点,揭示肺癌耐药性产生的分子机制,进一步完善对患者的预后和风险评估,促进肺癌精准医学的发展。本文对 scRNA-seq在NSCLC中的应用和研究进展进行综述。

    Abstract

    Lung cancer,of which non-small cell lung cancer(NSCLC)is the most common,has the highest mortality rate worldwide. Tumor immunotherapy,represented by CAR-T cell therapy and immune checkpoint blocking,has become a frontier hot spot in the field of cancer research. However,due to the tumor immunosuppressive microenvironment,the lack of tumor specific antigen and drug resistance,the therapeutic effect is still not ideal,and it’s urgent to explore new molecular targets and related molecular mechanisms. Single-cell RNA sequencing(scRNA-seg)is a new technology for high-throughput sequencing of transcriptome at the single-cell level, which can study gene expression in a single cell to understand the function and state of the cell. Using this technology,it is expected to find new targets for the cancer treatment and further understand the mechanism of tumor occurrence and development. This review summarizs the application and research progress of scRNA-seq in NSCLC.

  • 肺癌是死亡率最高的癌症,每年约有 160万人死于肺癌[1-2]。肺癌也是中国最常见且严重威胁人民健康的恶性肿瘤。最新数据统计,中国肺癌发病率为 57.26/10 万,病死率为 45.87/10 万[3]。肺癌按照病理类型可分为两类:小细胞肺癌和非小细胞肺癌 (non⁃small cell lung cancer,NSCLC),其中NSCLC是肺癌的主要病理类型,包括鳞癌、腺癌和大细胞癌。

  • 在过去10年中,免疫疗法的成功极大改变了肿瘤学领域的研究和治疗格局,以免疫检查点阻断为代表的免疫疗法有着显著的临床反应[4]。近年来针对程序性细胞死亡受体1(programmed cell death 1, PD1)/程序性细胞死亡配体 1(programmed cell death ligand 1,PD ⁃ L1)的免疫检查点阻断剂(im⁃ mune checkpoint blockade,ICB)在治疗多种类型人类肿瘤方面取得了较好的临床疗效[5-6],多种抗 PD⁃1/PD⁃L1 抗体药物已被美国食品药品监督管理局(food and drug administration,FDA)批准用于晚期 NSCLC 患者的二线治疗[7]。表皮生长因子受体 (epidermal growth factor receptor,EGFR)激酶反复突变以及间变性淋巴瘤激酶(anaplastic lymphoma kinase,ALK)融合的发现,也给NSCLC患者的治疗带来了显著变化[8-9]。虽然靶向治疗改善了部分NSCLC 患者的临床预后,但其5年生存率仍低于20%。对于无敏感基因突变的肺癌患者,靶向药物疗效不佳、预后不良,且耐药性限制了这些药物的临床治疗效果[10]。因此,寻找肺癌新的分子靶点、了解 NSCLC的耐药机制对于促进肿瘤的免疫治疗、改善患者的生存预后至关重要。

  • 高通量单细胞 RNA 测序(single ⁃cell RNA sequencing,scRNA⁃seq)数据统计分析改变了以往人们对细胞研究的方式。细胞生活在一个动态环境中,与周围环境相互作用、相互影响。主成分分析 (principal component analysis,PCA)可以识别已知和未知的细胞簇;在二维投影归一化基因表达谱后,进行细胞层次重建;以单细胞基因表达数据推断细胞特异性基因调控网络,并结合单细胞拟时序分析,从而可以在二维空间中聚类基因表达信息。 scRNA⁃seq数据统计分析有3个方向,分别为细胞类型鉴定、推断基因调控网络以及细胞层次结构的重构。近年来,scRNA⁃seq 技术已被广泛应用于多种癌症的发生发展机制研究,包括皮肤黑色素瘤[11]、 NSCLC[12-13]、肝细胞癌[14-15]、基底细胞癌[16]、结直肠癌[17-18]和乳腺癌[19]等。这些研究全面描绘了肿瘤异质性图谱;发现了肿瘤免疫特征、不同的免疫抑制人群、较少定义的免疫细胞亚群以及肿瘤相关信号转导网络;鉴定了驱动耐药性、预测转移风险和介导可塑性的细胞群等[20]。本文将scRNA⁃seq在NSLCL中的应用和研究进展进行综述。

  • 1 揭示NSCLC微环境

  • 肿瘤微环境(tumor microenvironment,TME)是肿瘤细胞与周围组织、基质细胞相互作用形成的一个复杂的综合系统。肿瘤细胞与TME 内基质细胞的相互作用不仅影响肿瘤的生长,还影响肿瘤的侵袭和预后[21-22]。肿瘤细胞缺乏均一性,进一步导致肺癌的靶向治疗难度加大。scRNA⁃seg可以有效识别和描述TME 内细胞的变化和特殊类型的细胞亚群,从而有助于改善肿瘤治疗效果和评价转归预后。

  • 研究人员分别从拷贝数变异(copy number vari⁃ ation,CNV)和基因表达谱(gene expression profile, GEP)两个维度解析 NSCLC 的异质性,把肺腺癌 (lung adencocarcinoma,LUAD)患者和肺鳞形细胞癌 (lung squamous cell carcinome,LUSC)患者分成 LUADm(腺癌有突变)、LUADn(腺癌无突变)和 LUSCn(鳞癌无突变)3组进行分析,发现LUSC患者的肿瘤细胞在降维分析后形成了多个特异性聚类,提示LUSC的癌细胞异质性高于LUAD;此外免疫细胞在样本中的差异也很大,LUAD 样本显示强烈的炎性微环境,T 细胞几乎占50%。除此之外NSCLC 的异质性还体现在肺上皮细胞,肺上皮细胞聚类分析揭示了两个不同的Ⅱ型肺泡上皮细胞(alveolar type2 cell,AT2)簇,其中,相比AT2⁃2细胞,AT2⁃1细胞高表达CEACAM6、KITLG、FOXC1等促进细胞增殖迁移的基因,意味着可向恶性肿瘤转化。研究还揭示了肿瘤异质性与肿瘤中性粒细胞的相关性,细胞组成和肿瘤亚型的关联分析发现中性粒细胞在 LUSC 中的浸润比例更高,且表现出一定的髓样抑制细胞(myeloid derived suppressor cell,MDSC)特征,如表达人类多形核髓样抑制细胞(polymorphonu⁃ clear MDSC,PMN⁃MDSC)的标志物 LOX⁃1,体现了肿瘤浸润中性粒细胞功能的多样性和复杂性[23]

  • 肿瘤相关中性粒细胞(tumor⁃associated neutro⁃ phil,TAN)的增加与NSCLC的恶性进展和预后不良有关这一结论在其他肺癌研究中也得到证实,并提示中性粒细胞浸润受肿瘤内在驱动机制调控。研究人员构建了一种转录因子 Sox2 过表达而肿瘤抑制因子 Lkb1 丢失的 NSCLC 小鼠模型(SL 小鼠),接着在 SL 小鼠中敲除 Nkx2⁃1 构建 SNL 小鼠,发现在 SNL小鼠内耗尽TAN可减少鳞状细胞癌的发生,这表明TAN与鳞状细胞癌的发生密切相关[24-25]。既往研究显示中性粒细胞多、瘤内异质性高这两个因素是肺癌免疫治疗效果不好的独立指标,中性粒细胞数量和肿瘤异质性可能是肺癌免疫治疗呈现差异的关键因素[26-27]

  • Lambrechts 等[28]利用scRNA⁃seq 验证了NSCLC 基质细胞之间存在很强的个体间差异,并对内皮细胞、成纤维细胞等在内的主要类型基质细胞进行了详细的研究。通过对比癌和癌旁组织,发现肿瘤内皮细胞的抗原呈递和免疫细胞归巢相关基因(如 CCL2、CCL18、ICAM1)被下调,有助于形成肿瘤免疫耐受;癌组织中存在异于癌旁组织的成纤维细胞类型,表达独特的胶原蛋白和细胞外基质分子(如 COL1A1 和 COL4A1),进一步对成纤维细胞进行功能分析,发现其高表达参与调控血管生成和肌生成的相关基因(如 MEG2C、MYH11、ITGA7),揭示了 TME内成纤维细胞的功能特异性。

  • 一些研究表明,TME中免疫细胞的类型、密度和位置在疾病进展中起着核心作用[29-30]。Sharma等[31]分析了 LUAD、癌旁组织和循环血液中不同免疫细胞谱系的分布,发现肿瘤组织中含有更多数量的免疫细胞,这些免疫细胞包含所有主要的免疫细胞谱系,最为丰富的是淋巴细胞和单核巨噬细胞,而NK 细胞的占比显著降低。除此之外,还观察到与CX3CL1趋化因子相关的单核巨噬细胞的浸润。对来自 14 例未接受治疗的NSCLC患者的 12 346 个 T 细胞进行深度 scRNA⁃seq,进一步揭示了 T 细胞在肺癌中的变化,发现除了肿瘤浸润性 CD8+ T 细胞经历衰竭之外,还确定了两个可能处在衰竭前状态的 CD8+ T细胞簇群,且与肺癌预后相关,高度“耗竭”T细胞由于其表观遗传变化对ICB表现出耐药性,而“预耗竭” T细胞则与较好的预后相关;研究还观察到肿瘤调节性 T 细胞(Treg)的异质性,其特征在于 TN⁃ FRSF9 的双峰分布,IL1R2 表达阳性的肿瘤 Treg 激活与 NSCLC 的预后不良相关[13]。这些研究详细描述了 TME 内细胞的变化,将有助于进一步了解 NSCLC中TME内细胞的功能状态和动力学,更好地实现肿瘤分层和精准治疗。

  • 2 发现新的NSCLC相关基因和信号通路

  • NSCLC 的发生发展机制尚未明确,scRNA⁃seq 可以鉴定出在肿瘤发生发展过程中发挥关键作用的异常基因和信号通路,为靶向治疗药物的研发提供理论依据。EGFR基因突变是NSCLC最常见的突变基因,检出率高达51.33%[32],EGFR酪氨酸激酶抑制剂(EGFR⁃tyrosine kinase inhibitor,EGFR⁃TKI)是 EGFR突变晚期NSCLC的标准治疗药物,然而由于 EGFR突变的晚期NSCLC具有高度异质性,往往存在1种基因以上的共突变,可通过多重分子机制促进耐药肿瘤细胞克隆增殖,导致临床治疗效果不佳。研究人员通过对最初存在和不存在EGFR突变的肺癌细胞同时进行基因组分析,发现大多数肿瘤患者晚期阶段都产生了EGFR突变。然后进一步定义了阻碍EGFR抑制剂反应的新途径,包括Wnt/β⁃ catenin信号通路改变和细胞周期基因CDK4、CDK6 的突变[33]。在NSCLC中,Wnt/β⁃catenin信号通路与肿瘤发生和细胞损伤后的再生有关,因此,Wnt/β⁃ catenin通路可能作为肺癌免疫治疗的靶点[34-35]。除此之外,研究人员对分别带有EGFR、KRAS 和多个致癌驱动突变的 3 例 NSCLC 患者肿瘤样本进行 scRNA⁃seq,发现带有EGFR突变患者癌细胞簇的特征是NPC2和表面活性剂基因(SFTPB、SFTPC、SFT⁃ PD)高表达;且成纤维细胞与其他2例患者的成纤维细胞相比,含有富含亮氨酸的小分子蛋白多糖(small leucine rich proteoglycan,SLRP)家族和丝氨酸蛋白酶抑制剂(serpin F1)的成员,表明这些成纤维细胞可能通过激活 Wnt 信号通路等方式参与AT2 细胞衍生为癌细胞的过程,为NSCLC的治疗提供了新的研究方向[36]

  • 肿瘤相关巨噬细胞(tumor ⁃ associated macro⁃ phage,TAM)在NSCLC进展中具有重要作用,scRNA⁃ seq发现在 M2 样 TAM 中,mincle 基因高表达;敲除 mincle 基因之后可观察到 M1 样 TAM 占比显著增加;过继转移mincle基因沉默的骨髓来源的巨噬细胞可显著抑制肿瘤进展,以上研究结果表明TAM中 mincle基因可作为NSCLC治疗的潜在分子靶点[37]。 Sinjab 等[38]对早期 LUAD scRNA⁃seq样本进行互作分析,发现上皮细胞的免疫检查点蛋白 CD24 与巨噬细胞的SIGLEC10以及树突状细胞的HAVCR2之间的相互作用增加,进一步分析CD24基因,发现其在正常肺组织、肺部非典型腺瘤样增生和肺肿瘤中表达逐渐增加,且与促肿瘤和免疫抑制相关基因 (如TIGIT、CTLA4、FOXP3)的表达呈正相关,而与抗肿瘤免疫标志物(如GZMB、GZMH、PRF1)的表达呈负相关;CRISPR 技术敲除 CD24或使用 CD24中和抗体后,小鼠LUAD细胞的生长被显著抑制,因此CD24 可能作为LUAD免疫治疗的一个新的分子靶点。

  • 3 用于NSCLC代谢组学研究

  • 机体细胞正常的新陈代谢包括合成与分解代谢,其中包含糖类、脂类、氨基酸等各种代谢通路,它们通过产生能量、完成生物合成分解来影响细胞的生长和凋亡。然而在肿瘤形成前期,这些细胞的代谢相关基因可被重新编程,导致其克隆性异常增生,逐步发展为肿瘤细胞并促进其增殖[39]

  • 早期发现NSCLC是提高患者生存率的关键,目前尚无可靠的血液检测指标用于早期NSCLC 的诊断,研究人员对5例早期NSCLC患者的组织样本进行 scRNA⁃seq,发现脂质代谢在不同细胞类型中广泛失调。通过分析肿瘤细胞与正常上皮细胞间的差异基因,发现肿瘤细胞下调的基因主要富集在脂代谢相关途径。对聚类得到的9种上皮细胞分别进行分析,发现与正常上皮细胞相比,肿瘤细胞中甘油三酯代谢、甘油磷脂代谢、脂肪酸合成和不饱和脂肪酸合成等多条代谢通路均发生异常变化,其中甘油三酯和甘油磷脂代谢相关基因在早期肺癌患者的多种类型细胞中下调,并最终确定了9种脂质,包含 3 种溶血磷脂酰胆碱、5 种磷脂酰胆碱和 1 种甘油三酯,作为早期肺癌检测的指标,这可能使 NSCLC患者获益于早期诊断,进而提高NSCLC患者的生存率[40]

  • 循环肿瘤细胞(circulating tumor cell,CTC)可用来实时监测肿瘤动态、评估治疗效果,实现精准个体治疗。己糖激酶 2(hexokinase2,HK2)作为一个与肿瘤 Warburg 效应密切相关的酶,可用于检测血液中高糖酵解活性的肿瘤细胞。这一标志物与血清肌酸激酶(creatine kinase,CK)联合检测可大幅提升Ⅲ/Ⅳ期 NSCLC 患者外周血中 CTC 的检出率,其原因在于新标志物能够发现 NSCLC 患者血液中广泛存在的之前被忽略的 CK 阴性 CTC。scRNA⁃seq 表明 CK 阴性 CTC 与 CK 阳性 CTC 有相同的全基因组拷贝数变异谱,揭示它们具有相同的起源[41]

  • 还有学者构建了一个基于铁死亡相关基因的肺癌预后模型,同时评估了预后模型的性能。通过癌组织和癌旁组织之间的差异分析,获得 6 497 个差异表达基因(differentially expressed gene,DEG),并将这些 DEG 与 NCBI 和 KEGG 数据库中 1 164 个铁死亡相关基因的共同基因确定为候选铁死亡相关基因,然后通过单因素COX分析筛选出283个候选模型铁死亡相关基因,最终确定了21个与患者预后显著相关的铁死亡相关基因,用于预后风险模型的建立。接着通过ROC曲线分析、PCA分析等验证了该模型具有良好的预测能力,可以准确地区分高危和低危人群,且与预后相关的基因(如 CAV1、 CDC25C、CHEK2、DKK1、E2E7)可作为 NSCLC 患者总生存时间独立的预后指标[42]

  • 4 揭示NSCLC耐药分子机制

  • ICB的出现使NSCLC的治疗策略转向以基因改变引导的靶向治疗,如 EGFR⁃TKI 阿法替尼和奥西美替尼以及 ALK 抑制剂阿列替尼、克唑替尼的使用,使肺癌的临床治疗效果得到了显著改善。同时肿瘤耐药性的出现已成为临床治疗面临的重大挑战,新的治疗策略或药物在提高患者生存率方面的需求尚未得到满足。

  • Aissa 等[36]在研究 NSCLC 患者对 TKI 产生耐药的过程中发现经过靶向药物治疗之后,肿瘤细胞的基因表达亚型和细胞周期均发生了变化;不同用药手段有利于抗性基因,如MAPK级联激活,CALD1、 CCDC80、TPM1、TACSTD2 恢复表达等,意味着这种药物诱导的耐药其实是可逆的;同时在不同NSCLC 细胞系中均观察到抗凋亡基因、NF⁃κB和MAPK通路在早期耐药细胞中激活,提示在不同肺癌细胞系中早期耐药基因表达有一定的共性,选择合适的以及合理使用靶向药物可能通过下调特定的细胞存活机制来减缓肿瘤细胞群的耐药性。除此之外, Kashima 等[43]通过对肺癌组织样本和 EGFR⁃TKI 耐药细胞模型的 scRNA ⁃ seq 检测,发现除 AURKA、 VIM 和 AXL 等已知耐药基因之外,CD74 在 EGFR⁃ TKI耐药中也发挥了重要作用,其上调会诱导细胞的耐药性并抑制细胞凋亡。研究者还发现 TME 中的巨噬细胞而非肿瘤细胞表达的表皮调节素可诱发 EGFR⁃TKI耐药,提示TME的细胞与肿瘤细胞的相互作用也是诱发耐药的重要因素之一[44]

  • 顺铂(CDDP)和培美曲塞(PEM)联合化疗是 LUAD 的主要治疗方案之一,然而药物抗性的出现极大地限制了其治疗效果。研究人员发现 miR⁃6077为LUAD 中CDDP/PEM 抗性的关键驱动因素。对接受CDDP/PEM联合治疗的LUAD患者样本进行 scRNA⁃seq,发现 CDDP/PEM 可诱导 cdkn1 和 keap1 上调,实验证明 miR⁃6077 可以通过 cdkn1a⁃cdk1 介导细胞周期停滞和 keap1⁃nrf2⁃slc7a11/nqo1 介导铁死亡,保护LUAD细胞免受 CDDP/PEM 诱导的细胞死亡,从而在体外和体内导致LUAD细胞的耐药,这一发现为克服LUAD的耐药性提供了一个新的治疗策略[45]

  • 5 用于NSCLC预后分析与风险评估

  • NSCLC 患者的生存预后问题是患者和家属关注的最主要问题,由于肿瘤的异质性导致进行精准的预后评估有一定难度。研究人员选取18例LUAD 患者的癌组织、癌旁组织和血液样本进行 scRNA⁃ seq,研究发现I期肿瘤就可以使得T细胞和NK细胞发生显著改变,T细胞在TME中的比例更高,NK细胞的比例则显著降低,提示不良预后。此外还建立了早期未治疗LUAD的免疫细胞图谱,揭示在早期病变中富含PPARγhiCD64hiCD14hi PPARγhiIL⁃6hi 巨噬细胞、CD1C阳性单核细胞、NK细胞和颗粒酶B阳性效应细胞,这些细胞同时存在于免疫微环境中,与患者预后相关。PPARγhiCD64hiCD14hi PPARγhiIL⁃6hi 巨噬细胞和细胞颗粒酶B阳性效应细胞显著减少的患者有明显的生存劣势[12]

  • Maynard 等[46]选取了靶向药物治疗前、治疗过程中肿瘤控制期、进展期 3 种 LUAD 样本进行 scRNA⁃seq,首先分析了所有样本突变率与总生存期之间的关系,发现突变率低总是与总生存期长相关。进一步研究发现3组样本细胞存在的共同特征是肿瘤细胞表面受体酪氨酸激酶信号保持着持续激活状态,包括常见的EGFR家族,该生长信号是维持细胞持续增殖的驱动因素,贯穿着肿瘤治疗的全过程。接着针对进展期组细胞进行深入分析,发现该组存在3种特殊的细胞亚群,分别是高表达犬尿氨酸通路的肿瘤细胞、IDO1+ 巨噬细胞和高表达细胞通讯基因(GJB2、GJB3、GJB5)的肿瘤细胞,这3种细胞群的特征均与较差的预后相关。肿瘤控制期组呈现较好的预后趋势,与治疗前组和进展期组的差异显著,其肿瘤细胞高表达肺泡信号相关基因,其中多个基因的高表达提示预后更好。通过组间对比,在肿瘤控制期组的AT2中发现NKX2⁃1特异性高表达,这或许可以作为肺癌预后较好的标志物[46]

  • 肿瘤风险预测模型可作为预测患者预后的工具。研究人员使用 scRNA⁃seq 检测基因共表达网络,构建了肿瘤风险预测模型,发现较低免疫浸润、 P53信号通路和Treg 细胞的激活是预后差的原因。此外,此模型还具有预测免疫治疗效果的功能[47]。还有一些研究人员通过scRNA⁃seq探究肿瘤细胞干性指数及干性相关基因与患者预后的关系,发现癌组织的干性指数高于癌旁组织,且干性指数与肿瘤分期呈正相关。另外,还发现并鉴定了 CCNA2、 CCNB1、FEN1、SPAG5 等 15 个干性相关基因,其过表达与肿瘤组织中较少的免疫细胞浸润有关,这些基因具有成为预后指标和治疗靶点的潜力[48-49]

  • 6 小结和展望

  • scRNA⁃seq从生命活动的基本功能单位——单细胞的维度帮助剖析复杂的TME,给实体瘤研究提供了新的思路。scRNA⁃seq可以精准捕获肺癌异质性和TME内丰富的生物信息,发现新的肿瘤相关基因和信号通路,从而帮助研究人员更深入地了解肺癌的发生发展和治疗耐药的关键生物学机制,有助于指导临床上对肺癌的早期诊断、靶向治疗、疗效监测与预后评估。未来通过对 NSCLC 单细胞数据的深入挖掘,可以探索更多新的治疗靶点以及疗效预测标志物等,更好地实现患者分层,同时也为精准医学的过程管理和复发监测提供可能。相信随着研究队列的逐渐扩大和循证医学证据的不断积累,scRNA⁃seq 必将为肺癌的精准治疗打开新篇章。

  • 参考文献

    • [1] GANDHI L,RODRÍGUEZ ⁃ ABREU D,GADGEEL S,et al.Pembrolizumab plus chemotherapy in metastatic non ⁃ small⁃cell lung cancer[J].N Engl J Med,2018,378(22):2078-2092

    • [2] SIEGEL R L,MILLER K D,JEMAL A.Cancer statistics,2019[J].CA A Cancer J Clin,2019,69(1):7-34

    • [3] CAO M M,CHEN W Q.Epidemiology of lung cancer in China[J].Thorac Cancer,2019,10(1):3-7

    • [4] TOPALIAN S L,TAUBE J M,ANDERS R A,et al.Mecha⁃ nism ⁃ driven biomarkers to guide immune checkpoint blockade in cancer therapy[J].Nat Rev Cancer,2016,16(5):275-287

    • [5] RIBAS A,WOLCHOK J D.Cancer immunotherapy using checkpoint blockade[J].Science,2018,359(6382):1350-1355

    • [6] 王梦玮,徐天蔚,王朝霞.PD⁃1/PD⁃L1免疫检查点抑制剂在肺癌临床研究中的进展[J].南京医科大学学报(自然科学版),2021,41(7):1084-1094

    • [7] HERBST R S,BAAS P,KIM D W,et al.Pembrolizumab versus docetaxel for previously treated,PD ⁃ L1⁃ positive,advanced non⁃small⁃cell lung cancer(KEYNOTE⁃010):a randomised controlled trial [J].Lancet,2016,387(10027):1540-1550

    • [8] SODA M,CHOI Y L,ENOMOTO M,et al.Identification of the transforming EML4⁃ALK fusion gene in non⁃small⁃ cell lung cancer[J].Nature,2007,448(7153):561-566

    • [9] PAEZ J G,JÄNNE P A,LEE J C,et al.EGFR mutations in lung cancer:correlation with clinical response to gefi⁃ tinib therapy[J].Science,2004,304(5676):1497-1500

    • [10] TSUI D W Y,MURTAZA M,WONG A S C,et al.Dyna⁃ mics of multiple resistance mechanisms in plasma DNA during EGFR ⁃ targeted therapies in non ⁃ small cell lung cancer[J].EMBO Mol Med,2018,10(6):7945

    • [11] SADE ⁃FELDMAN M,YIZHAK K,BJORGAARD S L,et al.Defining T cell states associated with response to checkpoint immunotherapy in melanoma[J].Cell,2019,176(1/2):404

    • [12] LAVIN Y,KOBAYASHI S,LEADER A,et al.Innate im⁃ mune landscape in early lung adenocarcinoma by paired single⁃cell analyses[J].Cell,2017,169(4):750-765

    • [13] GUO X Y,ZHANG Y Y,ZHENG L T,et al.Global chara⁃ cterization of T cells in non⁃small⁃cell lung cancer by sin⁃ gle⁃cell sequencing[J].Nat Med,2018,24(7):978-985

    • [14] ZHENG C H,ZHENG L T,YOO J K,et al.Landscape of infiltrating T cells in liver cancer revealed by single ⁃cell sequencing[J].Cell,2017,169(7):1342-1356

    • [15] ZHANG Q M,HE Y,LUO N,et al.Landscape and dyna⁃ mics of single immune cells in hepatocellular carcinoma [J].Cell,2019,179(4):829-845

    • [16] YOST K E,SATPATHY A T,WELLS D K,et al.Clonal replacement of tumor ⁃ specific T cells following PD ⁃ 1 blockade[J].Nat Med,2019,25(8):1251-1259

    • [17] ZHANG L,YU X,ZHENG L T,et al.Lineage tracking re⁃ veals dynamic relationships of T cells in colorectal cancer [J].Nature,2018,564(7735):268-272

    • [18] PENTER L,DIETZE K,RITTER J,et al.Localization⁃as⁃ sociated immune phenotypes of clonally expanded tumor⁃ infiltrating T cells and distribution of their target antigens in rectal cancer[J].OncoImmunology,2019,8(6):e1586409

    • [19] SAVAS P,VIRASSAMY B,YE C Z,et al.Single⁃cell pro⁃ filing of breast cancer T cells reveals a tissue ⁃ resident memory subset associated with improved prognosis[J].Nat Med,2018,24(7):986-993

    • [20] NEFTEL C,LAFFY J,FILBIN M G,et al.An integrative model of cellular states,plasticity,and genetics for glio⁃ blastoma[J].Cell,2019,178(4):835-849

    • [21] SON B,LEE S,YOUN H,et al.The role of tumor microen⁃ vironment in therapeutic resistance[J].Oncotarget,2017,8(3):3933-3945

    • [22] BALKWILL F R,CAPASSO M,HAGEMANN T.The tu⁃ mor microenvironment at a glance[J].J Cell Sci,2012,125(23):5591-5596

    • [23] WU F Y,FAN J,HE Y Y,et al.Single⁃cell profiling of tu⁃ mor heterogeneity and the microenvironment in advanced non ⁃ small cell lung cancer[J].Nat Commun,2021,12(1):2540

    • [24] KARGL J,BUSCH S E,YANG G H Y,et al.Neutrophils dominate the immune cell composition in non ⁃ small cell lung cancer[J].Nat Commun,2017,8:14381

    • [25] MOLLAOGLU G,JONES A,WAIT S J,et al.The lineage⁃ defining transcription factors SOX2 and NKX2 ⁃ 1 deter⁃ mine lung cancer cell fate and shape the tumor immune microenvironment[J].Immunity,2018,49(4):764-779

    • [26] WOLF Y,BARTOK O,PATKAR S,et al.UVB ⁃induced tumor heterogeneity diminishes immune response in mela⁃ noma[J].Cell,2019,179(1):219-235

    • [27] KARGL J,ZHU X D,ZHANG H J,et al.Neutrophil con⁃ tent predicts lymphocyte depletion and anti ⁃ PD1 treat⁃ ment failure in NSCLC [J].JCI Insight,2019,4(24):e130850

    • [28] LAMBRECHTS D,WAUTERS E,BOECKX B,et al.Phe⁃ notype molding of stromal cells in the lung tumor microen⁃ vironment[J].Nat Med,2018,24(8):1277-1289

    • [29] CHEN X,WAN J,LIU J K,et al.Increased IL⁃17⁃produc⁃ ing cells correlate with poor survival and lymphangiogene⁃ sis in NSCLC patients[J].Lung Cancer,2010,69(3):348-354

    • [30] WELSH T J,GREEN R H,RICHARDSON D,et al.Mac⁃ rophage and mast⁃cell invasion of tumor cell islets confers a marked survival advantage in non⁃small⁃cell lung cancer [J].J Clin Oncol,2005,23(35):8959-8967

    • [31] SHARMA P,ALLISON J P.Immune checkpoint targeting in cancer therapy:toward combination strategies with cu⁃ rative potential[J].Cell,2015,161(2):205-214

    • [32] 严晓娣,史国振,毛旭华.高通量测序技术检测非小细胞肺癌相关驱动基因的突变[J].南京医科大学学报(自然科学版),2021,41(2):193-197

    • [33] BLAKELY C M,WATKINS T B K,WU W,et al.Evolu⁃ tion and clinical impact of co ⁃ occurring genetic altera⁃ tions in advanced ⁃ stage EGFR ⁃mutant lung cancers[J].Nat Genet,2017,49(12):1693-1704

    • [34] TAMMELA T,SANCHEZ⁃RIVERA F J,CETINBAS N M,et al.A Wnt ⁃ producing niche drives proliferative poten⁃ tial and progression in lung adenocarcinoma[J].Nature,2017,545(7654):355-359

    • [35] KRISHNAMURTHY N,KURZROCK R.Targeting the Wnt/beta ⁃ catenin pathway in cancer:update on effectors and inhibitors[J].Cancer Treat Rev,2018,62:50-60

    • [36] AISSA A F,ISLAM A B M M K,ARISS M M,et al.Single ⁃ cell transcriptional changes associated with drug toler⁃ ance and response to combination therapies in cancer[J].Nat Commun,2021,12:1628

    • [37] LI C,XUE V W,WANG Q M,et al.The mincle/syk/nf⁃κb signaling circuit is essential for maintaining the protumoral activities of tumor ⁃ associated macrophages[J].Cancer Immunol Res,2020,8(8):1004-1017

    • [38] SINJAB A,HAN G C,TREEKITKARNMONGKOL W,et al.Resolving the spatial and cellular architecture of lung adenocarcinoma by multiregion single ⁃ cell sequencing [J].Cancer Discov,2021,11(10):2506-2523

    • [39] DUARTE I F,ROCHA C M,GIL A M.Metabolic profil⁃ ing of biofluids:potential in lung cancer screening and di⁃agnosis[J].Expert Rev Mol Diagn,2013,13(7):737-748

    • [40] WANG G,QIU M,XING X,et al.Lung cancer scrna⁃seq and lipidomics reveal aberrant lipid metabolism for early⁃ stage diagnosis[J].Sci Transl Med,2022,14(630):eabk2756

    • [41] YANG L,YAN X W,CHEN J,et al.Hexokinase 2 dis⁃ cerns a novel circulating tumor cell population associated with poor prognosis in lung cancer patients[J].Proc Natl Acad Sci U S A,2021,118(11):e2012228118

    • [42] HAN K,WANG J K,QIAN K,et al.Construction of a prognostic model for non⁃small⁃cell lung cancer based on ferroptosis ⁃ related genes[J].Biosci Rep,2021,41(5):BSR20210527

    • [43] KASHIMA Y,SHIBAHARA D,SUZUKI A,et al.Single⁃ cell analyses reveal diverse mechanisms of resistance to EGFR tyrosine kinase inhibitors in lung cancer[J].Can⁃ cer Res,2021,81(18):4835-4848

    • [44] MA S Q,ZHANG L,REN Y,et al.Epiregulin confers EGFR ⁃ TKI resistance via EGFR/ErbB2 heterodimer in non⁃small cell lung cancer[J].Oncogene,2021,40(14):2596-2609

    • [45] BI G S,LIANG J Q,ZHAO M N,et al.miR ⁃ 6077 pro⁃motes cisplatin/pemetrexed resistance in lung adenocarci⁃ noma via CDKN1A/cell cycle arrest and KEAP1/ferroptosis pathways[J].Mol Ther Nucleic Acids,2022,28:366-386

    • [46] MAYNARD A,MCCOACH C E,ROTOW J K,et al.Therapy⁃ induced evolution of human lung cancer revealed by sin⁃ gle⁃cell RNA sequencing[J].Cell,2020,182(5):1232-1251

    • [47] WU Q,WANG L,WEI H G,et al.Integration of multiple key molecules in lung adenocarcinoma identifies prognos⁃ tic and immunotherapeutic relevant gene signatures[J].Int Immunopharmacol,2020,83:106477

    • [48] ZHAO M N,CHEN Z C,ZHENG Y S,et al.Identification of cancer stem cell⁃related biomarkers in lung adenocarci⁃ noma by stemness index and weighted correlation network analysis[J].J Cancer Res Clin Oncol,2020,146(6):1463-1472

    • [49] ZENG H,JI J R,SONG X D,et al.Stemness related genes revealed by network analysis associated with tumor im⁃ mune microenvironment and the clinical outcome in lung adenocarcinoma[J].Front Genet,2020,11:549213

  • 参考文献

    • [1] GANDHI L,RODRÍGUEZ ⁃ ABREU D,GADGEEL S,et al.Pembrolizumab plus chemotherapy in metastatic non ⁃ small⁃cell lung cancer[J].N Engl J Med,2018,378(22):2078-2092

    • [2] SIEGEL R L,MILLER K D,JEMAL A.Cancer statistics,2019[J].CA A Cancer J Clin,2019,69(1):7-34

    • [3] CAO M M,CHEN W Q.Epidemiology of lung cancer in China[J].Thorac Cancer,2019,10(1):3-7

    • [4] TOPALIAN S L,TAUBE J M,ANDERS R A,et al.Mecha⁃ nism ⁃ driven biomarkers to guide immune checkpoint blockade in cancer therapy[J].Nat Rev Cancer,2016,16(5):275-287

    • [5] RIBAS A,WOLCHOK J D.Cancer immunotherapy using checkpoint blockade[J].Science,2018,359(6382):1350-1355

    • [6] 王梦玮,徐天蔚,王朝霞.PD⁃1/PD⁃L1免疫检查点抑制剂在肺癌临床研究中的进展[J].南京医科大学学报(自然科学版),2021,41(7):1084-1094

    • [7] HERBST R S,BAAS P,KIM D W,et al.Pembrolizumab versus docetaxel for previously treated,PD ⁃ L1⁃ positive,advanced non⁃small⁃cell lung cancer(KEYNOTE⁃010):a randomised controlled trial [J].Lancet,2016,387(10027):1540-1550

    • [8] SODA M,CHOI Y L,ENOMOTO M,et al.Identification of the transforming EML4⁃ALK fusion gene in non⁃small⁃ cell lung cancer[J].Nature,2007,448(7153):561-566

    • [9] PAEZ J G,JÄNNE P A,LEE J C,et al.EGFR mutations in lung cancer:correlation with clinical response to gefi⁃ tinib therapy[J].Science,2004,304(5676):1497-1500

    • [10] TSUI D W Y,MURTAZA M,WONG A S C,et al.Dyna⁃ mics of multiple resistance mechanisms in plasma DNA during EGFR ⁃ targeted therapies in non ⁃ small cell lung cancer[J].EMBO Mol Med,2018,10(6):7945

    • [11] SADE ⁃FELDMAN M,YIZHAK K,BJORGAARD S L,et al.Defining T cell states associated with response to checkpoint immunotherapy in melanoma[J].Cell,2019,176(1/2):404

    • [12] LAVIN Y,KOBAYASHI S,LEADER A,et al.Innate im⁃ mune landscape in early lung adenocarcinoma by paired single⁃cell analyses[J].Cell,2017,169(4):750-765

    • [13] GUO X Y,ZHANG Y Y,ZHENG L T,et al.Global chara⁃ cterization of T cells in non⁃small⁃cell lung cancer by sin⁃ gle⁃cell sequencing[J].Nat Med,2018,24(7):978-985

    • [14] ZHENG C H,ZHENG L T,YOO J K,et al.Landscape of infiltrating T cells in liver cancer revealed by single ⁃cell sequencing[J].Cell,2017,169(7):1342-1356

    • [15] ZHANG Q M,HE Y,LUO N,et al.Landscape and dyna⁃ mics of single immune cells in hepatocellular carcinoma [J].Cell,2019,179(4):829-845

    • [16] YOST K E,SATPATHY A T,WELLS D K,et al.Clonal replacement of tumor ⁃ specific T cells following PD ⁃ 1 blockade[J].Nat Med,2019,25(8):1251-1259

    • [17] ZHANG L,YU X,ZHENG L T,et al.Lineage tracking re⁃ veals dynamic relationships of T cells in colorectal cancer [J].Nature,2018,564(7735):268-272

    • [18] PENTER L,DIETZE K,RITTER J,et al.Localization⁃as⁃ sociated immune phenotypes of clonally expanded tumor⁃ infiltrating T cells and distribution of their target antigens in rectal cancer[J].OncoImmunology,2019,8(6):e1586409

    • [19] SAVAS P,VIRASSAMY B,YE C Z,et al.Single⁃cell pro⁃ filing of breast cancer T cells reveals a tissue ⁃ resident memory subset associated with improved prognosis[J].Nat Med,2018,24(7):986-993

    • [20] NEFTEL C,LAFFY J,FILBIN M G,et al.An integrative model of cellular states,plasticity,and genetics for glio⁃ blastoma[J].Cell,2019,178(4):835-849

    • [21] SON B,LEE S,YOUN H,et al.The role of tumor microen⁃ vironment in therapeutic resistance[J].Oncotarget,2017,8(3):3933-3945

    • [22] BALKWILL F R,CAPASSO M,HAGEMANN T.The tu⁃ mor microenvironment at a glance[J].J Cell Sci,2012,125(23):5591-5596

    • [23] WU F Y,FAN J,HE Y Y,et al.Single⁃cell profiling of tu⁃ mor heterogeneity and the microenvironment in advanced non ⁃ small cell lung cancer[J].Nat Commun,2021,12(1):2540

    • [24] KARGL J,BUSCH S E,YANG G H Y,et al.Neutrophils dominate the immune cell composition in non ⁃ small cell lung cancer[J].Nat Commun,2017,8:14381

    • [25] MOLLAOGLU G,JONES A,WAIT S J,et al.The lineage⁃ defining transcription factors SOX2 and NKX2 ⁃ 1 deter⁃ mine lung cancer cell fate and shape the tumor immune microenvironment[J].Immunity,2018,49(4):764-779

    • [26] WOLF Y,BARTOK O,PATKAR S,et al.UVB ⁃induced tumor heterogeneity diminishes immune response in mela⁃ noma[J].Cell,2019,179(1):219-235

    • [27] KARGL J,ZHU X D,ZHANG H J,et al.Neutrophil con⁃ tent predicts lymphocyte depletion and anti ⁃ PD1 treat⁃ ment failure in NSCLC [J].JCI Insight,2019,4(24):e130850

    • [28] LAMBRECHTS D,WAUTERS E,BOECKX B,et al.Phe⁃ notype molding of stromal cells in the lung tumor microen⁃ vironment[J].Nat Med,2018,24(8):1277-1289

    • [29] CHEN X,WAN J,LIU J K,et al.Increased IL⁃17⁃produc⁃ ing cells correlate with poor survival and lymphangiogene⁃ sis in NSCLC patients[J].Lung Cancer,2010,69(3):348-354

    • [30] WELSH T J,GREEN R H,RICHARDSON D,et al.Mac⁃ rophage and mast⁃cell invasion of tumor cell islets confers a marked survival advantage in non⁃small⁃cell lung cancer [J].J Clin Oncol,2005,23(35):8959-8967

    • [31] SHARMA P,ALLISON J P.Immune checkpoint targeting in cancer therapy:toward combination strategies with cu⁃ rative potential[J].Cell,2015,161(2):205-214

    • [32] 严晓娣,史国振,毛旭华.高通量测序技术检测非小细胞肺癌相关驱动基因的突变[J].南京医科大学学报(自然科学版),2021,41(2):193-197

    • [33] BLAKELY C M,WATKINS T B K,WU W,et al.Evolu⁃ tion and clinical impact of co ⁃ occurring genetic altera⁃ tions in advanced ⁃ stage EGFR ⁃mutant lung cancers[J].Nat Genet,2017,49(12):1693-1704

    • [34] TAMMELA T,SANCHEZ⁃RIVERA F J,CETINBAS N M,et al.A Wnt ⁃ producing niche drives proliferative poten⁃ tial and progression in lung adenocarcinoma[J].Nature,2017,545(7654):355-359

    • [35] KRISHNAMURTHY N,KURZROCK R.Targeting the Wnt/beta ⁃ catenin pathway in cancer:update on effectors and inhibitors[J].Cancer Treat Rev,2018,62:50-60

    • [36] AISSA A F,ISLAM A B M M K,ARISS M M,et al.Single ⁃ cell transcriptional changes associated with drug toler⁃ ance and response to combination therapies in cancer[J].Nat Commun,2021,12:1628

    • [37] LI C,XUE V W,WANG Q M,et al.The mincle/syk/nf⁃κb signaling circuit is essential for maintaining the protumoral activities of tumor ⁃ associated macrophages[J].Cancer Immunol Res,2020,8(8):1004-1017

    • [38] SINJAB A,HAN G C,TREEKITKARNMONGKOL W,et al.Resolving the spatial and cellular architecture of lung adenocarcinoma by multiregion single ⁃ cell sequencing [J].Cancer Discov,2021,11(10):2506-2523

    • [39] DUARTE I F,ROCHA C M,GIL A M.Metabolic profil⁃ ing of biofluids:potential in lung cancer screening and di⁃agnosis[J].Expert Rev Mol Diagn,2013,13(7):737-748

    • [40] WANG G,QIU M,XING X,et al.Lung cancer scrna⁃seq and lipidomics reveal aberrant lipid metabolism for early⁃ stage diagnosis[J].Sci Transl Med,2022,14(630):eabk2756

    • [41] YANG L,YAN X W,CHEN J,et al.Hexokinase 2 dis⁃ cerns a novel circulating tumor cell population associated with poor prognosis in lung cancer patients[J].Proc Natl Acad Sci U S A,2021,118(11):e2012228118

    • [42] HAN K,WANG J K,QIAN K,et al.Construction of a prognostic model for non⁃small⁃cell lung cancer based on ferroptosis ⁃ related genes[J].Biosci Rep,2021,41(5):BSR20210527

    • [43] KASHIMA Y,SHIBAHARA D,SUZUKI A,et al.Single⁃ cell analyses reveal diverse mechanisms of resistance to EGFR tyrosine kinase inhibitors in lung cancer[J].Can⁃ cer Res,2021,81(18):4835-4848

    • [44] MA S Q,ZHANG L,REN Y,et al.Epiregulin confers EGFR ⁃ TKI resistance via EGFR/ErbB2 heterodimer in non⁃small cell lung cancer[J].Oncogene,2021,40(14):2596-2609

    • [45] BI G S,LIANG J Q,ZHAO M N,et al.miR ⁃ 6077 pro⁃motes cisplatin/pemetrexed resistance in lung adenocarci⁃ noma via CDKN1A/cell cycle arrest and KEAP1/ferroptosis pathways[J].Mol Ther Nucleic Acids,2022,28:366-386

    • [46] MAYNARD A,MCCOACH C E,ROTOW J K,et al.Therapy⁃ induced evolution of human lung cancer revealed by sin⁃ gle⁃cell RNA sequencing[J].Cell,2020,182(5):1232-1251

    • [47] WU Q,WANG L,WEI H G,et al.Integration of multiple key molecules in lung adenocarcinoma identifies prognos⁃ tic and immunotherapeutic relevant gene signatures[J].Int Immunopharmacol,2020,83:106477

    • [48] ZHAO M N,CHEN Z C,ZHENG Y S,et al.Identification of cancer stem cell⁃related biomarkers in lung adenocarci⁃ noma by stemness index and weighted correlation network analysis[J].J Cancer Res Clin Oncol,2020,146(6):1463-1472

    • [49] ZENG H,JI J R,SONG X D,et al.Stemness related genes revealed by network analysis associated with tumor im⁃ mune microenvironment and the clinical outcome in lung adenocarcinoma[J].Front Genet,2020,11:549213

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