高级搜索

食管鳞癌免疫检查点抑制剂疗效预测标志物的研究进展

陈容珊, 薛丽燕

陈容珊, 薛丽燕. 食管鳞癌免疫检查点抑制剂疗效预测标志物的研究进展[J]. 肿瘤防治研究, 2022, 49(12): 1302-1306. DOI: 10.3971/j.issn.1000-8578.2022.22.0450
引用本文: 陈容珊, 薛丽燕. 食管鳞癌免疫检查点抑制剂疗效预测标志物的研究进展[J]. 肿瘤防治研究, 2022, 49(12): 1302-1306. DOI: 10.3971/j.issn.1000-8578.2022.22.0450
CHEN Rongshan, XUE Liyan. Research Progress of Predictive Markers for Efficacy of Immune Checkpoint Inhibitors for Esophageal Squamous Cell Carcinoma[J]. Cancer Research on Prevention and Treatment, 2022, 49(12): 1302-1306. DOI: 10.3971/j.issn.1000-8578.2022.22.0450
Citation: CHEN Rongshan, XUE Liyan. Research Progress of Predictive Markers for Efficacy of Immune Checkpoint Inhibitors for Esophageal Squamous Cell Carcinoma[J]. Cancer Research on Prevention and Treatment, 2022, 49(12): 1302-1306. DOI: 10.3971/j.issn.1000-8578.2022.22.0450

食管鳞癌免疫检查点抑制剂疗效预测标志物的研究进展

基金项目: 

中国医学科学院医学与健康科技创新工程项目 2021-I2M-1-067

国家癌症中心/中国医学科学院肿瘤医院人才项目希望之星 

详细信息
    作者简介:

    陈容珊(1996-),女,硕士在读,主要从事食管癌病理研究

    通讯作者:

    薛丽燕(1976-),女,博士,主任医师,主要从事胸部和上消化道肿瘤病理研究,E-mail: xuely@cicams.ac.cn

  • 中图分类号: R735.1

Research Progress of Predictive Markers for Efficacy of Immune Checkpoint Inhibitors for Esophageal Squamous Cell Carcinoma

Funding: 

CAMS Innovation Fund for Medical Sciences 2021-I2M-1-067

Hope Star of Talent Program of National Cancer Center/Cancer Hospital of Chinese Academy of Medical Sciences 

More Information
  • 摘要:

    食管鳞癌是全球食管癌的主要病理类型,确诊时通常已为晚期,致死率高且预后差。新辅助治疗后手术切除是食管鳞癌的主要治疗方法。随着免疫治疗的兴起,以免疫检查点抑制剂(ICIs)为主的免疫治疗能给食管鳞癌患者带来一定的生存获益。但食管鳞癌免疫治疗的有效率有限,寻找能够预测免疫治疗效果的生物标志物有助于筛选免疫治疗可能受益患者。本文结合当今免疫治疗及相关标志物的研究文献,对食管鳞癌ICIs治疗相关预测标志物的研究进展作一综述,希望能为食管鳞癌的精准治疗和预后判断提供帮助。

     

    Abstract:

    Esophageal squamous cell carcinoma (ESCC) is the main prevalent histological type of esophageal cancer worldwide, with a high mortality and poor prognosis due to advanced stage at diagnosis. Surgical resection after neoadjuvant therapy is the main treatment for ESCC. With the rise of immunotherapy, immunotherapy on ESCC has been shown to improve outcomes, especially the immune checkpoint inhibitors (ICIs). As the efficacy of immunotherapy for ESCC is limited, it is helpful to screen patients who may benefit from immunotherapy by looking for predictive biomarkers of efficacy of immunotherapy. In this paper, based on the current literature on immunotherapy and related biomarkers, we review the research progress on predictive markers of ICIs for ESCC, in hopes of providing assistances for the precise treatment and prognosis determination.

     

  • 子宫内膜癌是在北美和欧洲最常见的女性恶性肿瘤,近年来我国的发病率明显增加,发病人群年轻化[1]。子宫内膜癌的发病机制目前尚未完全明确,随着研究的深入,越来越清晰地认识到子宫内膜癌是多种因素协同交叉作用、具有不同遗传和分子特征的一类疾病[2-3]。根据肿瘤发病机制与雌激素依赖相关性,将其分为Ⅰ型和Ⅱ型,80%以上为Ⅰ型子宫内膜癌。遗传易感性是导致个体对相同致癌因素敏感度不一致的重要因素,某些雌激素代谢通路中关键酶可以改变体内雌激素或外源性雌激素及其代谢产物的水平。基因多态性与酶活性有关,雌激素代谢酶基因多态性可能导致子宫内膜癌发病易感性差异[4]。本研究利用SNP分型检测技术,探索雌激素代谢关键酶CYP1B1、CYP1A1和NQO1基因的单核苷酸多态性(single nucleotide polymorphisms, SNPs)位点分布频率与Ⅰ型子宫内膜癌易感性之间关系,以期对Ⅰ型子宫内膜癌的易感人群进行筛查。

    抽取我院2014年3月—2016年10月收治的经病理诊断为子宫内膜样腺癌的103例患者和同期子宫内膜正常的100例其他疾病患者静脉外周全血2~3 ml,作为检测标本。病例和对照组均取得患者的知情同意并详细询问病史,包括初潮年龄、生育状况、绝经年龄、孕产次数、内科并发症及肿瘤家族史。测量身高与体重,并计算体质指数(body mass index, BMI),测量血压,采集静脉血化验空腹血糖(FPG)及血脂、肿瘤标志物[5-6]。年龄31~78岁,平均(51.15±9.69)岁,子宫内膜癌组平均年龄(54.77±8.21)岁,对照组为(46.95±9.65)岁。

    由于Ⅰ型子宫内膜癌为雌激素依赖性肿瘤,结合相关文献报道,在美国国家生物技术信息中心(National Center for Biotechnology Information, NCBI)人类基因组数据库中选择与雌激素代谢相关的关键酶CYP1A1、CYP1B1,具有抗癌突变的依赖还原型辅酶Ⅰ/Ⅱ醌氧化还原酶1(quinone oxido-reductase1, NQO1)3个基因,选取与代谢酶活性密切相关且杂合度大于10%的SNPs位点进行基因型分析。

    利用引物设计软件premier 5.0,在含SNPs的DNA序列上设计PCR引物,并将设计的引物进行引物的同源性比较,选出同源性最小而Tm值和G/C比值合适的引物作为PCR反应引物,所扩增片段应包含要检测的SNPs位点,其位置尽可能设计在PCR扩增片段的中部。

    使用DNA提取试剂盒(DP304-03)从外周血中提取DNA,进行预扩增,琼脂糖凝胶电泳检测DNA完整性。根据设计引物模板,进行延伸反应及延伸产物纯化。PCR扩增反应体系总体积为50 μl,内含5 μl 10×PCR缓冲液,3 μl 25 mmol/L MgCl2溶液,5 μl 2 mmol/L dNTP混合物,1.25 U Taq DNA聚合酶,0.5 μmol/L引物及100g的基因组DNA。反应在MJ公司PTC-100 PCR反应仪中进行。PCR循环条件为95℃预变性10 min,然后94℃变性30 s,60℃复性30 s,72℃延伸30 s,反应40个循环后,72℃再延伸10 min。扩增产物用2%的琼脂糖凝胶电泳检测,4℃保存。PCR产物经纯化后作为测序模板,用Big-Dye末端荧光标记试剂盒进行测序反应,测序反应产物经纯化后在ABI公司3730XL测序仪上进行测序电泳,用Gene codes公司的Sequencer4.2对测序结果进行分析。

    采用SPSS18.0软件对实验数据进行统计学分析,用χ2检验和多因素Logistic回归模型分析各基因型在两组人群中的分布差异及其与子宫内膜癌临床病理特征的相关性,P < 0.05为差异有统计学意义。

    CYP1B1基因SNPs位点rs111888224因无碱基改变未纳入统计,rs1056836、rs2551188、rs10916在研究人群中均存在多态性,但分布频率差异无统计学意义;rs1056836 C、G基因分布频率差异有统计学意义(P=0.0454)。

    CYP1A1基因SNP位点rs4646421在研究人群中存在CT、CC、TT多态性。与CT型相比,CC型为保护基因型,OR=0.479(95%CI: 0.255~0.899),差异有统计学意义(P=0.0219),其C、T基因分布频率差异有统计学意义(P=0.0041),见表 1

    表  1  基因型/等位基因在Ⅰ型子宫内膜癌患者中的分布(n(%))
    Table  1  Genotype/alleles distributions in typeⅠendometrial carcinoma cases (n(%))
    下载: 导出CSV 
    | 显示表格

    对CYP1A1基因rs4646421位点不同基因型与Ⅰ型子宫内膜癌的危险因素相关性进行分层比较,发现在年龄 > 60岁、BMI≥25、绝经延迟、并发高血压的个体中,携带CT+TT突变基因型将增加罹患Ⅰ型子宫内膜癌的风险(P < 0.05),见表 2

    表  2  发病危险因素与基因型分布的关系(n(%))
    Table  2  Association between risk factors and genotypes distributions of SNP(rs4646421) in CYP1A1 (n(%))
    下载: 导出CSV 
    | 显示表格

    雌激素分布在细胞内外表面,通过雌激素受体的核内结合蛋白在靶细胞中保持高亲和力和特异性。涉及雌激素生物合成和代谢的基因的多态性是雌激素受体阳性恶性肿瘤的潜在危险因素,有几种细胞色素P450(CYP)酶参与雌激素的氧化代谢途径[7]。CYP1A1和CYP1B1是代谢产生2-羟基-4-羟基雌激素代谢物的基本酶[8-9],在不同人群中不仅存在多态性,并且在性激素相关组织中的表达高于细胞色素P450超家族的其他成员,其基因多态性与乳腺癌、子宫肌瘤、子宫内膜异位症、宫颈癌等疾病风险相关[10-14]

    CYP1A1基因定位于人类染色体15q22-24,编码由512个氨基酸组成的蛋白质,是细胞色素P450家族中高诱导成员。CYP1A1酶的激活参与内源性和外源性化合物的氧化作用,催化多环芳香族碳氢化合物转化为酚与环氧化合物。一些酚类和环氧化合物可与DNA结合形成加合物,最终转化为致癌物、二醇环氧化物,通过环氧化物水解酶与一些抗肿瘤药物发生耐药,并增加个体肿瘤风险[9, 15]。本研究结果显示CYP1A1基因SNP位点rs4646421在子宫内膜癌人群中存在CT、CC、TT多态性,与携带CT基因型相比,携带CC基因型的个体罹患子宫内膜癌的风险较小,携带T等位基因的个体子宫内膜癌的发病风险高于C等位基因携带者。与携带CC基因型相比,携带TC+TT基因型在年龄 > 60岁、BMI≥25、绝经延迟(超过52岁)、合并高血压的女性中Ⅰ型子宫内膜癌的发病风险增加。

    CYP1B1基因位于染色体2q21-22,其单核苷酸多态性SNP总数有353个,目前研究较多的是SNPrs1056836,其mRNA发生1294位碱基胞嘧啶(cytosine, C)变异为鸟嘌呤(guanine, G),使DNA两条链形成野生CC型,杂合CG型和变异GG型3种基因型,即基因多态性。这种改变导致第三外显子432密码子的碱基由CTG变异为GTG,由其编码的亮氨酸(leucine, Leu)变异为缬氨酸(valine, Val),导致CYP1B1酶蛋白功能的改变[16]。CYP1B1通过多方面影响肿瘤的易感性和预后,在胰岛素抵抗中产生作用,突变型表达的酶之蛋白质表现出更强的激活癌前物质活性,或影响睾酮的6β-羟化作用及性激素的激活代谢等[17-18]

    本研究中CYP1B1基因SNP rs2551188、rs10916两个位点在人群中存在多态性,但分布频率无统计学差异;rs1056836在正常子宫内膜组与子宫内膜癌两组间的基因型分布频率无差异,但发生更多的碱基胞嘧啶变异为鸟嘌呤,可能会使罹患子宫内膜癌的风险增加,以往文献中发现对于单个位点基因突变的表现为阴性结论时,多个位点联合的单倍体基因型研究却往往能得到阳性结论,似乎预示着CYP1B1基因在分子遗传学上有一定的倾向性[16-19],但其与肿瘤易感性及预后的机制问题还存在着诸多争议与疑惑,有待于进一步研究。

    NQO1是一种重要的化学致癌物质代谢酶,具有抗癌作用,可使内源性及外源性醌类化合物通过电子还原反应变成低毒性的氢醌类物质,再转化为水溶性化合物排出体外,降低了细胞发生突变及癌变的概率。如果位点发生突变,使原位编码的氨基酸发生改变,将会导致该酶活性减弱或完全丧失,降低解毒功能且不能维护细胞稳定,增加某些易感个体细胞发生癌变[20]。报道显示NQO1蛋白高表达在卵巢癌、乳腺癌、结直肠癌等肿瘤中,与临床分期晚、分化程度差和淋巴结转移等临床特征有关[21-22],本研究选择的SNP位点rs45488899和rs1800566虽在两组间均存在基因多态性,但分布频率不具有差异性,因此认为这两个SNP位点与Ⅰ型子宫内膜癌的发生无显著相关性。

    本研究通过基因表达谱差异的分析,推测CYP1A1基因rs4646421位点的多态性在Ⅰ型子宫内膜癌的发生发展中起着重要作用,可作为进行高危人群筛查的基因位点,还可以突变的基因型为基础,采取针对性的基因靶向治疗,或者通过修正基因突变谱来改善其预后。但是,基因多态性受复杂的遗传、环境因素、种族等的影响,并与样本量的大小有关,必须经过大样本试验来进一步明确基因多态性与子宫内膜癌易感性之间的基因环境交互作用及具体机制。

    Competing interests: The authors declare that they have no competing interests.
    作者贡献:
    陈容珊:论文撰写与修改
    薛丽燕:论文审校
  • [1]

    Cao Y, Qin S, Luo S, et al. Pembrolizumab versus chemotherapy for patients with esophageal squamous cell carcinoma enrolled in the randomized KEYNOTE-181 trial in Asia[J]. ESMO Open, 2022, 7(1): 100341. doi: 10.1016/j.esmoop.2021.100341

    [2]

    Huang J, Xu J, Chen Y, et al. Camrelizumab versus investigator's choice of chemotherapy as second-line therapy for advanced or metastatic oesophageal squamous cell carcinoma (ESCORT): a multicentre, randomised, open-label, phase 3 study[J]. Lancet Oncol, 2020, 21(6): 832-842. doi: 10.1016/S1470-2045(20)30110-8

    [3]

    Sun JM, Shen L, Shah MA, et al. Pembrolizumab plus chemotherapy versus chemotherapy alone for first-line treatment of advanced oesophageal cancer (KEYNOTE-590): a randomised, placebo-controlled, phase 3 study[J]. Lancet, 2021, 398(10302): 759-771. doi: 10.1016/S0140-6736(21)01234-4

    [4]

    Yang H, Wang K, Wang T, et al. The Combination Options and Predictive Biomarkers of PD-1/PD-L1 Inhibitors in Esophageal Cancer[J]. Front Oncol, 2020, 10: 300. doi: 10.3389/fonc.2020.00300

    [5]

    Quigley DA, Kristensen V. Predicting prognosis and therapeutic response from interactions between lymphocytes and tumor cells[J]. Mol Oncol, 2015, 9(10): 2054-2062. doi: 10.1016/j.molonc.2015.10.003

    [6]

    Galon J, Bruni D. Approaches to treat immune hot, altered and cold tumours with combination immunotherapies[J]. Nat Rev Drug Discov, 2019, 18(3): 197-218. doi: 10.1038/s41573-018-0007-y

    [7]

    Teng MW, Ngiow SF, Ribas A, et al. Classifying Cancers Based on T-cell Infiltration and PD-L1[J]. Cancer Res, 2015, 75(11): 2139-2145. doi: 10.1158/0008-5472.CAN-15-0255

    [8]

    Helmink BA, Reddy SM, Gao J, et al. B cells and tertiary lymphoid structures promote immunotherapy response[J]. Nature, 2020, 577(7791): 549-555. doi: 10.1038/s41586-019-1922-8

    [9]

    Cabrita R, Lauss M, Sanna A, et al. Tertiary lymphoid structures improve immunotherapy and survival in melanoma[J]. Nature, 2020, 577(7791): 561-565. doi: 10.1038/s41586-019-1914-8

    [10]

    Ueno H, Kanemitsu Y, Sekine S, et al. A Multicenter Study of the Prognostic Value of Desmoplastic Reaction Categorization in Stage Ⅱ Colorectal Cancer[J]. Am J Surg Pathol, 2019, 43(8): 1015-1022. doi: 10.1097/PAS.0000000000001272

    [11]

    Kemi NA, Eskuri M, Pohjanen VM, et al. Histological assessment of stromal maturity as a prognostic factor in surgically treated gastric adenocarcinoma[J]. Histopathology, 2019, 75(6): 882-889. doi: 10.1111/his.13934

    [12] 李志文, 郑重, 张前, 等. 食管鳞状细胞癌肿瘤间质成熟度的临床病理学意义及与PD-L1表达的相关性[J]. 临床与实验病理学杂志, 2021, 37(8): 933-938. doi: 10.13315/j.cnki.cjcep.2021.08.008

    Li ZW, Zheng Z, Zhang Q, et al. Clinicopathological significance of tumor stromal maturity and relationship with PD-L1 expression in esophageal squamous cell cancer[J]. Lin Chuang Yu Shi Yan Bing Li Xue Za Zhi, 2021, 37(8): 933-938. doi: 10.13315/j.cnki.cjcep.2021.08.008

    [13]

    Obradovic A, Graves D, Korrer M, et al. Immunostimulatory cancer-associated fibroblast subpopulations can predict immunotherapy response in head and neck cancer[J]. Clin Cancer Res, 2022, 28(10): 2094-2109. doi: 10.1158/1078-0432.CCR-21-3570

    [14]

    Ayers M, Lunceford J, Nebozhyn M, et al. IFN-γ-related mRNA profile predicts clinical response to PD-1 blockade[J]. J Clin Invest, 2017, 127(8): 2930-2940. doi: 10.1172/JCI91190

    [15]

    Cristescu R, Mogg R, Ayers M, et al. Pan-tumor genomic biomarkers for PD-1 checkpoint blockade-based immunotherapy[J]. Science, 2018, 362(6411): eaar3593. doi: 10.1126/science.aar3593

    [16]

    Fukuchi M, Sakurai S, Suzuki M, et al. Esophageal squamous cell carcinoma with marked eosinophil infiltration[J]. Case Rep Gastroenterol, 2011, 5(3): 648-653. doi: 10.1159/000332441

    [17]

    Grisaru-Tal S, Itan M, Klion AD, et al. A new dawn for eosinophils in the tumour microenvironment[J]. Nat Rev Cancer, 2020, 20(10): 594-607. doi: 10.1038/s41568-020-0283-9

    [18]

    Le DT, Uram JN, Wang H, et al. PD-1 Blockade in Tumors with Mismatch-Repair Deficiency[J]. N Engl J Med, 2015, 372(26): 2509-2520. doi: 10.1056/NEJMoa1500596

    [19]

    Chang L, Chang M, Chang HM, et al. Microsatellite Instability: A Predictive Biomarker for Cancer Immunotherapy[J]. Appl Immunohistochem Mol Morphol, 2018, 26(2): e15-e21. doi: 10.1097/PAI.0000000000000575

    [20]

    Hewitt LC, Inam IZ, Saito Y, et al. Epstein-Barr virus and mismatch repair deficiency status differ between oesophageal and gastric cancer: A large multi-centre study[J]. Eur J Cancer, 2018, 94: 104-114. doi: 10.1016/j.ejca.2018.02.014

    [21]

    Le DT, Durham JN, Smith KN, et al. Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade[J]. Science, 2017, 357(6349): 409-413. doi: 10.1126/science.aan6733

    [22]

    Lemery S, Keegan P, Pazdur R. First FDA Approval Agnostic of Cancer Site - When a Biomarker Defines the Indication[J]. N Engl J Med, 2017, 377(15): 1409-1412. doi: 10.1056/NEJMp1709968

    [23]

    Marabelle A, Fakih M, Lopez J, et al. Association of tumour mutational burden with outcomes in patients with advanced solid tumours treated with pembrolizumab: prospective biomarker analysis of the multicohort, open-label, phase 2 KEYNOTE-158 study[J]. Lancet Oncol, 2020, 21(10): 1353-1365. doi: 10.1016/S1470-2045(20)30445-9

    [24]

    Lee M, Samstein RM, Valero C, et al. Tumor mutational burden as a predictive biomarker for checkpoint inhibitor immunotherapy[J]. Hum Vaccin Immunother, 2020, 16(1): 112-115. doi: 10.1080/21645515.2019.1631136

    [25]

    Samstein RM, Lee CH, Shoushtari AN, et al. Tumor mutational load predicts survival after immunotherapy across multiple cancer types[J]. Nat Genet, 2019, 51(2): 202-206. doi: 10.1038/s41588-018-0312-8

    [26]

    Balachandran VP, Łuksza M, Zhao JN, et al. Identification of unique neoantigen qualities in long-term survivors of pancreatic cancer[J]. Nature, 2017, 551(7681): 512-516. doi: 10.1038/nature24462

    [27]

    Richman LP, Vonderheide RH, Rech AJ. Neoantigen Dissimilarity to the Self-Proteome Predicts Immunogenicity and Response to Immune Checkpoint Blockade[J]. Cell Syst, 2019, 9(4): 375-382. e4. doi: 10.1016/j.cels.2019.08.009

    [28]

    Huang J, Xu B, Mo H, et al. Safety, Activity, and Biomarkers of SHR-1210, an Anti-PD-1 Antibody, for Patients with Advanced Esophageal Carcinoma[J]. Clin Cancer Res, 2018, 24(6): 1296-1304. doi: 10.1158/1078-0432.CCR-17-2439

    [29]

    Wang F, Ren C, Zhao Q, et al. Association of frequent amplification of chromosome 11q13 in esophageal squamous cell cancer with clinical benefit to immune check point blockade[J]. J Clin Oncol, 2019, 37(15_suppl): 4036. doi: 10.1200/JCO.2019.37.15_suppl.4036

    [30]

    Li L, Li M, Wang X. Cancer type-dependent correlations between TP53 mutations and antitumor immunity[J]. DNA Repair (Amst), 2020, 88: 102785. doi: 10.1016/j.dnarep.2020.102785

    [31]

    Zhang P, Huang Y. Genomic alterations in KMT2 family predict outcome of immune checkpoint therapy in multiple cancers[J]. J Hematol Oncol, 2021, 14(1): 39. doi: 10.1186/s13045-021-01050-0

    [32]

    Wang F, Zhao Q, Wang YN, et al. Evaluation of POLE and POLD1 Mutations as Biomarkers for Immunotherapy Outcomes Across Multiple Cancer Types[J]. JAMA Oncol, 2019, 5(10): 1504-1506. doi: 10.1001/jamaoncol.2019.2963

    [33]

    Sudo K, Kato K, Matsuzaki J, et al. Identification of serum microRNAs predicting the response of esophageal squamous-cell carcinoma to nivolumab[J]. Jpn J Clin Oncol, 2020, 50(2): 114-121.

    [34]

    Huang S, Zhang J, Lai X, et al. Identification of Novel Tumor Microenvironment-Related Long Noncoding RNAs to Determine the Prognosis and Response to Immunotherapy of Hepatocellular Carcinoma Patients[J]. Front Mol Biosci, 2021, 8: 781307. doi: 10.3389/fmolb.2021.781307

    [35]

    Luo YH, Yang YP, Chien CS, et al. Plasma Level of Circular RNA hsa_circ_0000190 Correlates with Tumor Progression and Poor Treatment Response in Advanced Lung Cancers[J]. Cancers (Basel), 2020, 12(7): 1740. doi: 10.3390/cancers12071740

    [36]

    Kato R, Yamasaki M, Urakawa S, et al. Increased Tim-3(+) T cells in PBMCs during nivolumab therapy correlate with responses and prognosis of advanced esophageal squamous cell carcinoma patients[J]. Cancer Immunol Immunother, 2018, 67(11): 1673-1683. doi: 10.1007/s00262-018-2225-x

    [37]

    Kim JY, Choi JK, Jung H. Genome-wide methylation patterns predict clinical benefit of immunotherapy in lung cancer[J]. Clin Epigenetics, 2020, 12(1): 119. doi: 10.1186/s13148-020-00907-4

    [38]

    Malczewski AB, Navarro S, Coward JI, et al. Microbiome-derived metabolome as a potential predictor of response to cancer immunotherapy[J]. J Immunother Cancer, 2020, 8(2): e001383. doi: 10.1136/jitc-2020-001383

    [39]

    Baiden-Amissah REM, Tuyaerts S. Contribution of Aging, Obesity, and Microbiota on Tumor Immunotherapy Efficacy and Toxicity[J]. Int J Mol Sci, 2019, 20(14): 3586. doi: 10.3390/ijms20143586

计量
  • 文章访问数:  1842
  • HTML全文浏览量:  453
  • PDF下载量:  643
  • 被引次数: 0
出版历程
  • 收稿日期:  2022-04-25
  • 修回日期:  2022-09-01
  • 网络出版日期:  2024-01-12
  • 刊出日期:  2022-12-24

目录

/

返回文章
返回
x 关闭 永久关闭