Article(id=1148989442200760320, tenantId=1146029695717560320, journalId=1146031712061968385, issueId=1148989441470952447, articleNumber=null, orderNo=null, doi=10.12211/2096-8280.2023-060, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1692892800000, receivedDateStr=2023-08-25, revisedDate=1699718400000, revisedDateStr=2023-11-12, acceptedDate=null, acceptedDateStr=null, onlineDate=1751870030212, onlineDateStr=2025-07-07, pubDate=1714406400000, pubDateStr=2024-04-30, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1751870030212, onlineIssueDateStr=2025-07-07, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1751870030212, creator=13701087609, updateTime=1751870030212, updator=13701087609, issue=Issue{id=1148989441470952447, tenantId=1146029695717560320, journalId=1146031712061968385, year='2024', volume='5', issue='2', pageStart='217', pageEnd='395', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1751870030037, creator=13701087609, updateTime=1752057315553, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1149774973969068078, tenantId=1146029695717560320, journalId=1146031712061968385, issueId=1148989441470952447, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1149774973969068079, tenantId=1146029695717560320, journalId=1146031712061968385, issueId=1148989441470952447, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=254, endPage=266, ext={EN=ArticleExt(id=1149999704932184097, articleId=1148989442200760320, tenantId=1146029695717560320, journalId=1146031712061968385, language=EN, title=Strategies for the design and optimization of tumor neoantigen vaccines, columnId=1149894683619635652, journalTitle=Synthetic Biology Journal, columnName=Invited Review, runingTitle=null, highlight=null, articleAbstract=

With the research progress and clinical application of immune checkpoint inhibitors and chimeric antigen receptor T-cell therapies, immunotherapy has substantially changed the treating modalities for various tumors. Tumor neoantigen vaccines, as a promising immunotherapy method, aim to trigger a novel T cell response against neoantigens. Neoantigens, with their high specificity, can induce and expand the tumor-specific T cell receptor repertoire, which were discovered through the second-generation sequencing of DNA extracted from both the patient’s tumor and non-tumor tissue samples. The sequences and HLA types are then analyzed for alignment to pinpoint tumor-specific mutations. To validate the significance of these mutations, RNA sequencing data are integrated with the results. Subsequently, bioinformatics platforms are employed for the prediction and analysis of neoantigens encoded by mutated genes and HLA types, enabling the identification of potential immunogenic neoantigens. Finally, the immunogenicity of these neoantigens is assessed through techniques such as ELISPOT and tetramer assays. Tumor vaccines can be categorized as peptide-based, DNA-based, RNA-based, and DC-based products. Viruses, lipid nanoparticles, and nano delivery systems can activate antigen-presenting cells, enhancing their ability to recognize and present tumor-associated antigens, thus promoting the activation of CD8+ T cells. Neoantigen vaccines can be administered through various routes, including subcutaneous injection, intramuscular injection, intraperitoneal injection, intradermal injection, intravenous injection, or intralymphatic injection. Preliminary clinical studies have shown that neoantigen tumor vaccines have demonstrated evidence of strong tumor-specific immunogenicity and antitumor activity. In this review, we summarize in detail the source, prediction, and identification of tumor neoantigens, as well as the classification and immunization scheme of neoantigen vaccines. In addition, we highlight strategies for optimizing tumor neoantigen vaccines, including prediction algorithms, expressing multiple epitope structures, increasing immunogenicity, administration methods and delivery systems, and combining adjuvants and various treatments, providing new insights for the development of personalized immunotherapy.

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随着免疫检查点抑制剂和嵌合抗原受体T细胞疗法在不同适应证中的研究和临床应用,免疫治疗已经彻底改变了多种肿瘤的治疗方式。肿瘤新抗原疫苗作为一种前景广阔的免疫治疗方法,旨在激发针对新抗原的特异性T细胞反应。新抗原具有高度特异性,能够诱导和扩展肿瘤特异性T细胞库,即表位扩展。初步临床研究表明,通过快速、经济、高效的合成生物学技术,新抗原肿瘤疫苗已经展现出强大的肿瘤特异性免疫原性和抗肿瘤活性的初步证据。本文详细探讨了肿瘤新抗原的来源、发现与鉴定,以及新抗原疫苗的分类和免疫接种方案。还总结了肿瘤新抗原疫苗的优化策略,包括对预测算法、疫苗结构、免疫原性、给药方式和递送系统等方面的优化,以及联合佐剂、放化疗、免疫检查点抑制剂等方式,为个性化免疫疗法的发展提供了新的思路。

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张斌(1970—),男,研究员,博士生导师。研究方向为血干细胞移植、干细胞研究与转化应用、肿瘤免疫细胞治疗等。E-mail:
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涂辉阳(1988—),男,博士研究生。研究方向为肿瘤免疫治疗。E-mail:

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肿瘤新抗原疫苗的设计与优化策略
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涂辉阳 1, 2 , 韩为东 1 , 张斌 3
合成生物学 | 特约评述 2024,5(2): 254-266
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合成生物学 | 特约评述 2024, 5(2): 254-266
肿瘤新抗原疫苗的设计与优化策略
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涂辉阳1, 2 , 韩为东1, 张斌3
作者信息
  • 1 中国人民解放军总医院第一医学中心生物治疗科,北京 100039
  • 2 九江市第一人民医院肿瘤科,江西 九江 332000
  • 3 中国人民解放军总医院第五医学中心血液病医学部,造血干细胞治疗及转化研究北京市重点实验室,北京 100071
  • 涂辉阳(1988—),男,博士研究生。研究方向为肿瘤免疫治疗。E-mail:

通讯作者:

张斌(1970—),男,研究员,博士生导师。研究方向为血干细胞移植、干细胞研究与转化应用、肿瘤免疫细胞治疗等。E-mail:
Strategies for the design and optimization of tumor neoantigen vaccines
Huiyang TU1, 2 , Weidong HAN1, Bin ZHANG3
Affiliations
  • 1 Department of Biotherapy,the First Medical Center,Chinese PLA General Hospital,Beijing 100039,China
  • 2 Department of Oncology,Jiujiang No. 1 People’s Hospital,Jiujiang 332000,Jiangxi,China
  • 3 Senior Department of Hematology,the Fifth Medical Center of Chinese PLA General Hospital,Beijing Key Laboratory of Hematopoietic Stem Cell Therapy and Translational Research,Beijing 100071,China
出版时间: 2024-04-30 doi: 10.12211/2096-8280.2023-060
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随着免疫检查点抑制剂和嵌合抗原受体T细胞疗法在不同适应证中的研究和临床应用,免疫治疗已经彻底改变了多种肿瘤的治疗方式。肿瘤新抗原疫苗作为一种前景广阔的免疫治疗方法,旨在激发针对新抗原的特异性T细胞反应。新抗原具有高度特异性,能够诱导和扩展肿瘤特异性T细胞库,即表位扩展。初步临床研究表明,通过快速、经济、高效的合成生物学技术,新抗原肿瘤疫苗已经展现出强大的肿瘤特异性免疫原性和抗肿瘤活性的初步证据。本文详细探讨了肿瘤新抗原的来源、发现与鉴定,以及新抗原疫苗的分类和免疫接种方案。还总结了肿瘤新抗原疫苗的优化策略,包括对预测算法、疫苗结构、免疫原性、给药方式和递送系统等方面的优化,以及联合佐剂、放化疗、免疫检查点抑制剂等方式,为个性化免疫疗法的发展提供了新的思路。

新抗原  /  肿瘤疫苗  /  免疫治疗  /  合成生物学  /  个体化治疗

With the research progress and clinical application of immune checkpoint inhibitors and chimeric antigen receptor T-cell therapies, immunotherapy has substantially changed the treating modalities for various tumors. Tumor neoantigen vaccines, as a promising immunotherapy method, aim to trigger a novel T cell response against neoantigens. Neoantigens, with their high specificity, can induce and expand the tumor-specific T cell receptor repertoire, which were discovered through the second-generation sequencing of DNA extracted from both the patient’s tumor and non-tumor tissue samples. The sequences and HLA types are then analyzed for alignment to pinpoint tumor-specific mutations. To validate the significance of these mutations, RNA sequencing data are integrated with the results. Subsequently, bioinformatics platforms are employed for the prediction and analysis of neoantigens encoded by mutated genes and HLA types, enabling the identification of potential immunogenic neoantigens. Finally, the immunogenicity of these neoantigens is assessed through techniques such as ELISPOT and tetramer assays. Tumor vaccines can be categorized as peptide-based, DNA-based, RNA-based, and DC-based products. Viruses, lipid nanoparticles, and nano delivery systems can activate antigen-presenting cells, enhancing their ability to recognize and present tumor-associated antigens, thus promoting the activation of CD8+ T cells. Neoantigen vaccines can be administered through various routes, including subcutaneous injection, intramuscular injection, intraperitoneal injection, intradermal injection, intravenous injection, or intralymphatic injection. Preliminary clinical studies have shown that neoantigen tumor vaccines have demonstrated evidence of strong tumor-specific immunogenicity and antitumor activity. In this review, we summarize in detail the source, prediction, and identification of tumor neoantigens, as well as the classification and immunization scheme of neoantigen vaccines. In addition, we highlight strategies for optimizing tumor neoantigen vaccines, including prediction algorithms, expressing multiple epitope structures, increasing immunogenicity, administration methods and delivery systems, and combining adjuvants and various treatments, providing new insights for the development of personalized immunotherapy.

neoantigens  /  tumor vaccines  /  immunotherapy  /  biosynthesis  /  personal therapy
涂辉阳, 韩为东, 张斌. 肿瘤新抗原疫苗的设计与优化策略. 合成生物学, 2024 , 5 (2) : 254 -266 . DOI: 10.12211/2096-8280.2023-060
Huiyang TU, Weidong HAN, Bin ZHANG. Strategies for the design and optimization of tumor neoantigen vaccines[J]. Synthetic Biology Journal, 2024 , 5 (2) : 254 -266 . DOI: 10.12211/2096-8280.2023-060
肿瘤免疫治疗通过重塑肿瘤微环境,恢复机体的抗肿瘤免疫反应,实现对肿瘤的控制1。肿瘤疫苗作为一种主动性免疫疗法,其原理是疫苗被抗原呈递细胞识别后,激活免疫系统,诱导产生CD4+ T细胞和CD8+ T细胞,进而识别和消灭表达这些抗原的肿瘤细胞。早期的治疗性疫苗主要靶向在肿瘤中普遍存在的过度表达的抗原,即肿瘤相关抗原(tumor associated antigen,TAA)2。然而,TAA的特异性差,有“非肿瘤靶向效应”(on target-off tumor)风险,易诱发自身免疫性疾病3。肿瘤新抗原(neoantigens)是在肿瘤发生发展过程中形成的,来源于肿瘤细胞中突变基因的表达,被称为肿瘤特异性抗原(tumor specific antigen,TSA)4。新抗原疫苗不仅能引发新抗原特异性T细胞的产生,而且不会导致机体产生免疫耐受5,还能增强已存在的新抗原特异性T细胞反应,提升其对肿瘤细胞的杀伤力,并扩大肿瘤特异性T细胞反应的范围和克隆多样性6。个体化肿瘤新抗原疫苗和抗PD-1单抗组合获得了FDA突破疗法认定,用于高复发风险的Ⅲ/Ⅳ期黑色素瘤术后辅助治疗,显示了肿瘤新抗原疫苗个性化治疗肿瘤的潜力7
肿瘤新抗原疫苗仍面临着一系列挑战,如免疫逃逸、免疫耐受等。临床研究正在不断改进疫苗的基本设计,以期提高抗肿瘤疗效。肿瘤新抗原疫苗的设计涵盖了新抗原的类型、疫苗形式、免疫佐剂、免疫方案以及递送系统等多个方面要素8。本文将详细探讨肿瘤新抗原的来源、鉴定与验证,新抗原疫苗的分类,以及免疫接种方案等,并对新抗原疫苗优化策略进行综述。
新抗原是由肿瘤细胞中出现的基因变异产生,包括基因组的单核苷酸变异(SNV)、基因融合、转录组的选择性剪接、RNA编辑和转录组非编码区的突变等9-11。其中,作为产生肿瘤新抗原中最常见的类型,SNV是由于肿瘤细胞的遗传不稳定性所致。非同义突变基因编码的多肽能够通过MHC分子呈递到肿瘤细胞表面,从而形成肿瘤新抗原。例如,IDH1(R132H)突变是脑胶质瘤中的常见突变,使用该突变位点生成的肿瘤新抗原多肽疫苗(IDH1-vac)可以治疗WHO Ⅲ、Ⅳ级星形细胞瘤,诱导了多种MHC等位基因的患者免疫应答12。肿瘤细胞基因组的结构变异还包括易位、缺失、重复和基因融合等,这些变异会改变开放阅读框,从而形成肿瘤新抗原13。另外,选择性剪接可以导致mRNA中的外显子被排除或内含子被保留,从而形成新抗原。在非小细胞肺癌中,转录组异常剪接会产生多种异构体多肽,经过NetMHC模型的预测和酶联免疫斑点(ELISPOT)验证,已经鉴定出了一些潜在的肿瘤新抗原14
新抗原在机体内被免疫系统识别,与B细胞或T细胞结合的抗原部位被称为新抗原表位。新抗原的预测包括新抗原表位与HLA结合、pMHC与TCR结合、抗原表位与BCR结合的亲和力,还有表位的亲水性、结合稳定性、基因突变等因素。B细胞表位(B-cell epitope)是结合免疫球蛋白或抗体的抗原部分。B细胞表位的早期预测方法通常基于物理化学特征的氨基酸倾向量表(amino acid propensity scales),具体参数包括残基亲水性、灵活性、表面可及性和β转角等15。PREDITOP量表基于氨基酸的亲水性、可接近性、灵活性和二级结构特性等参数算法来预测线性B细胞表位16。PEOPLE量表加入了β转角参数17。基于机器学习(ML)的方法可以提高预测概率,常用的方法包括BepiPred18、ABCpred19、LBtope20等。例如,BepiPred通过随机森林训练方法从抗原-抗体复合物的3D结构预测B细胞表位18
新抗原被提取后经蛋白酶体剪切成8~11个氨基酸的肽,与抗原加工相关转运蛋白(TAP)穿梭到内质网中,并呈现到新合成的主要组织相容性复合物Ⅰ类(MHC-Ⅰ)和MHC-Ⅱ类分子上,分别被CD8+ T和CD4+ T细胞识别。新抗原的预测通过生物信息学工具模拟新抗原在体内的处理过程,包括蛋白酶体剪切、TAP转运以及肽-MHC结合等步骤21。肽-MHC结合是决定T细胞表位的最具选择性因素。为了预测HLA等位基因的肽结合亲和力,T细胞表位常用的模型有NetChop模型、NetCTL模型、NetMHC模型和NetMHCpan模型等。NetChop模型可以预测蛋白酶体的剪切位点,NetCTL模型可以预测蛋白酶体剪切位点、TAP转运效率以及肽段和MHC-Ⅰ结合的亲和力,NetMHC模型可以基于神经网络的方法预测肽段和MHC-Ⅰ结合的亲和力,通常需要对特定等位基因进行50~100次实验确定的肽结合测试,以建立具有足够精度的模型22-24。NetMHCpan模型则整合了pMHC亲和力和质谱洗脱配体数据,为所有等位基因训练一个共同的模型,从而预测新抗原能诱导机体产生免疫响应的概率25
肿瘤新抗原的鉴定流程是对患者肿瘤和非肿瘤组织样本的DNA进行二代测序,比对分析后发现肿瘤特异性突变位点。结合RNA测序结果,确认突变的有效性。然后,利用生物信息学平台预测分析突变基因编码的新抗原,筛选出潜在的免疫原性新抗原。最后,通过ELISPOT、四聚体等技术评估新抗原的免疫原性,确定其能激活免疫系统并引发肿瘤特异性免疫应答26-29。在一项多肽疫苗治疗局部晚期或晚期黑色素瘤的研究中,干扰素(IFN-γ)ELISPOT实验发现外周血单个核细胞(PBMC)对预测的抗原表位呈现了强烈的免疫应答,胞内细胞因子染色(intracellular cytokine staining)提示大多数IFN-γ阳性PBMC应答是由CD4+ T细胞所产生。此外,在体外扩增实验(in vitro expansion)中,六位患者至少有一个抗原表位(EPT)池能够在体外扩增后诱导出IFN-γ分泌的免疫反应30。有研究对接种黑色素瘤多肽疫苗后冰冻保存8~14年的引流淋巴结进行了评估,经过分离收获单个核细胞后,B细胞ELISPOT实验检测出了具有分泌疫苗特异性抗体的B细胞31
新抗原肿瘤疫苗最常见于多肽/蛋白的疫苗,包括长肽(15~31个氨基酸)和短肽(8~10个氨基酸)。这类疫苗序列明确,制备和保存简单,能直接与MHC分子结合,引发强烈的CD8+ T细胞反应。但多肽疫苗易降解,免疫原性弱,呈递能力低,导致Th1细胞反应能力降低,易产生免疫耐受,且制备周期较长32-35。一种载入黑色素瘤特异性TRP2180-188多肽的新型脂质体疫苗M/CpG-ODN-TRP2-Lipo通过上调DC表面的MHC Ⅱ类分子、CD80和CD86的表达增强了DC的活化,还减少了髓源性抑制细胞(MDSC)和调节性T细胞的数量,同时增加了活化T细胞、肿瘤新抗原特异性CD8+细胞毒性T细胞和产生干扰素-γ细胞的数量36
相较于多肽疫苗,蛋白疫苗含有更多抗原表位信息,免疫原性和稳定性更高,能同时诱导CD8+和CD4+ T细胞反应。在新抗原测序比对中,差异表达的蛋白可直接用于设计。肺癌CIMAvax疫苗是全球首个注册的用于晚期非小细胞肺癌的治疗性蛋白疫苗,2011年正式在古巴上市,一项真实世界研究入组了106名ⅢB、Ⅳ期非小细胞肺癌患者,经一线治疗缓解或疾病稳定后,开始接受CIMAvax疫苗治疗。结果显示:中位总生存期长达22.4个月,6个月的生存率为97.7%,12个月的生存率82.7%,24个月的生存率为45.5%37
核酸疫苗,包括DNA和mRNA疫苗,其通过引入编码肿瘤特异性抗原(TSA)的DNA或RNA片段到细胞中,并表达被免疫系统识别的抗原,从而引发免疫系统对肿瘤产生免疫应答。DNA疫苗具有结构稳定、设计制备快速的优势。但DNA疫苗的免疫原性相对较低,需要与免疫佐剂或其他免疫疗法联合应用,并且存在基因整合的风险38。临床前研究显示,肿瘤新抗原DNA疫苗能在体内诱导抗肿瘤反应,并能在体外直接杀伤肿瘤细胞,且主要产生MHCⅠ类分子限制的CD8+ T细胞反应39。一种脂质体包裹的多表位DNA疫苗诱导产生了肿瘤浸润性CD8+ T细胞,在小鼠模型中显著抑制黑色素瘤的生长并减少了肺转移的数量40。相比DNA疫苗,mRNA疫苗具有更强的免疫原性。mRNA疫苗的设计更为简单,能够快速调整和定制不同的抗原表达。mRNA疫苗具有较高的灵活性和快速生产周期,并且不需要进入细胞核,避免了可能的基因组整合和遗传毒性风险。mRNA-4157是一种脂质纳米颗粒个性化新抗原肿瘤疫苗,可编码多达34种新抗原。在Ⅰ期剂量爬坡临床试验中,13名患者接受了单药治疗,其中12名保持无病状态,中位随访时间为8个月。在Ⅱb期临床试验中,与PD-1单抗单药治疗相比,mRNA-4157疫苗联合PD-1单抗的疗法在Ⅲ、Ⅳ期黑色素瘤患者进行肿瘤完全切除后,可显著延长无复发生存期,并降低了44%的复发或死亡风险41。个体化mRNA疫苗在胃肠道肿瘤的治疗中展示了良好的安全性,并能在体内检测到新抗原特异性TCR42。然而,mRNA疫苗的结构相对不稳定,易降解,并且其表达持续时间较短,需要多次接种以维持免疫反应。
免疫系统中存在多种不同类型的树突状细胞(DC),包括常规DC(cDC)、浆细胞样DC(pDC)和由单核细胞衍生的DC(MoDC)43。其中,绝大多数DC疫苗使用体外培养产生的单核细胞衍生DC。DC疫苗的优势是本身作为抗原呈递细胞,能激活多种免疫细胞,且抗原加载方式多样,可根据患者需求选择合适的抗原呈递策略。然而,其制备过程复杂,包括获取树突细胞、选择和加载抗原等步骤,工艺复杂,成本和耗时较高,仅能在5%~15%的患者中实现有效的免疫反应44。同时,高度个性化也是其面临的挑战。Sipuleucel-T是唯一获得美国FDA批准的肿瘤DC疫苗,在转移性去势抵抗性前列腺癌患者中,与安慰剂组相比,该疫苗降低了22%的死亡风险45。一项由研究者发起的单臂、两个中心的试验(ChiCTR-ONC-16009100,NCT02956551),对纳入经过治疗的12名转移性肺癌患者,使用个体化新抗原脉冲自体DC疫苗治疗。总共进行了85次疫苗治疗,每人的中位数剂量为5次,所有与治疗相关的不良事件均为1~2级,中位无进展生存期(PFS)为5.5个月,中位总生存期(OS)为7.9个月46
免疫方案是影响肿瘤疫苗疗效的关键,涉及免疫剂量、方式、接种时间和周期等因素。适当的剂量能有效引发免疫应答,过低或过高则可能导致免疫耐受。一项Ⅰ/Ⅰb期临床试验入组了10例新确诊的脑胶质母细胞瘤患者,经手术切除和常规放疗后接种了个性化新抗原疫苗。免疫方案设计为在四周内(第1、4、8、15和22天)接种五剂基础免疫疫苗,8周和16周后再接种两剂加强疫苗。在未接受过地塞米松治疗的患者中产生了循环性多功能的新抗原特异性的CD4+ T细胞和CD8+ T细胞反应,单细胞TCR测序表明,来自外周血新抗原特异性T细胞能够迁移到颅内胶质母细胞瘤肿瘤中47。疫苗起效需要时间,受患者免疫功能影响,因此,早期接种肿瘤疫苗最能发挥其免疫作用48
新抗原疫苗的给药方式多样,包括皮下注射、肌内注射、腹腔注射、皮内注射、静脉注射或淋巴结内注射等。其中,皮下注射和肌内注射是最常用的方法。不同的给药途径会影响疫苗被各种细胞类型摄取,进而影响抗原向引流淋巴结的有效递送。在次级淋巴器官中,大量的免疫细胞(如APC和T细胞)密集存在,为有效诱导适应性免疫反应提供了理想的环境。其他注射方式也有报道,在NCT04266730中,多肽疫苗PANDA-VAC通过3次等体积的腹腔注射和1次腿部皮下注射方式给药,而在NCT02413645中则采用淋巴结内注射的方式49。接种部位的选择对抗原呈递也有影响。同一接种部位重复接种在疫苗接种部位微环境(vaccine-site microenvironment,VSME)能诱导产生B/T细胞区、成熟DC、高内皮小静脉和趋化因子的聚集,形成三级淋巴结构(tertiary lymphoid structure)50
为了优化疫苗的免疫原性和稳定性,通常采用联合佐剂、设计长肽疫苗、改进递送系统以及联合其他治疗手段等方法,这些策略不仅能够达到增强免疫效果的目的,而且起到协同增效的作用。
通过整合多种预测方法,包括肿瘤基因组数据,可以更精确地发现肿瘤特异性的突变位点和预测肿瘤新抗原。一种肿瘤表位免疫原性模型整合了呈递和识别相关肽的特征,包括MHC结合亲和力、MHC结合稳定性、肿瘤丰度和疏水性分数等参数,该模型以高于0.70的精度滤除了98%的非免疫原性肽,还从310个优先从肿瘤测序数据中筛选出的表位进行了T细胞结合测试,并得到了验证结果51。pTuneos是基于高通量测序数据进行肿瘤新抗原排序筛选的计算方法,通过整合新抗原产生、呈递和识别过程中的特征评估新抗原在肿瘤中的真实免疫原性。pTuneos在黑色素瘤疫苗队列数据和肿瘤浸润淋巴细胞(TIL)识别表位数据上进行了测试,证实了预测候选新抗原的MHC呈递和T细胞识别能力,并预测了SNV来源突变肽的免疫原性52。预测多肽-MHC结合ACME模型则将深度卷积神经网络与注意力模块相结合,整合了从卷积网络多个层次提取的特征,有效捕捉了多肽与MHC结合的内在特征,从而建立了一个准确且可解释的预测模型53。基于转移学习的pMHC-TCR结合预测网络(pMTnet)模型,可用于预测Ⅰ类主要组织相容性复合物所呈现的新抗原和T细胞抗原的TCR结合特异性并得到了验证。该模型还发现,在人类肿瘤基因组学数据中,新抗原通常比自身抗原更具免疫原性54
由于肿瘤抗原免疫原性低,单一表位抗原需要多次给药以产生良好免疫应答。多表位抗原疫苗,含有多个抗原表位,理论上能提供更广泛的抗原覆盖,增强免疫应答的广谱性和效力。在多肽疫苗设计方面,有研究合成了短肽、长肽的脂质体疫苗,分别是单表位短肽AE36和E75(HER2/neu源性肽段)的混合物、多表位长肽E75-AE36(短肽的连接)与Pan HLA-DR抗原(PADRE)肽的组合。通过在HER-2+TUBO肿瘤小鼠进行预防性和治疗性注射,相比短肽疫苗,脂质体多表位肽与PADRE疫苗可以显著增强CD4+和CD8+ T细胞应答,并且能分泌更多的IFN-γ55。一种新型多表位DNA新抗原疫苗平台将长表位与突变形式的泛素融合到新抗原的N端,临床前研究显示,优化的多表位新抗原DNA疫苗能够在转移性胰腺神经内分泌肿瘤患者中诱导新抗原特异性抗肿瘤免疫应答56。此外,增加肿瘤细胞表面MHC分子表达,或设计包含Ⅰ类和Ⅱ类限制性抗原模式的多肽疫苗,可有效诱导特异性CD8+ T细胞反应57。HLA-A 0201限制性的9个氨基酸SIM2(237~245)表位能有效诱导对SIM2表达的前列腺癌细胞株PC3的抗原特异性反应,因此有研究设计了包含MHC-Ⅰ和MHC-Ⅱ限制性表位的多肽疫苗。该疫苗能诱导CD8+ T细胞对HLA-A0201限制性SIM2(237~245)表位产生IFN-γ应答,同时,CD4+ T细胞对SIM2(240~254)表位产生IL-2应答。该疫苗还能诱导对表达SIM2的肿瘤细胞产生特异性CD8+ T细胞反应58
在设计mRNA疫苗模板时,不仅需要考虑抗原表位的长度,还可以引入非目标序列以增加免疫原性。外源性抗原通过MHC-Ⅱ分子呈递CD4+ T细胞,这个过程发生在MⅡC区间。当使用mRNA作为传递抗原的方法时,可以通过抗原序列与MHC-Ⅱ靶向信号[如不变链、溶酶体相关膜蛋白(LAMP)或DC-LAMP的信号序列]的耦合将抗原导向这些区室59-61。有研究将抗原序列与MHC Ⅰ类转运信号MITD耦合,也可以同时多位点扩增CD8和CD4 T细胞,从而产生抗原特异性CD8+ T细胞和广泛而多变的特异性CD4+ T细胞群62-63。基于此,mRNA的模板通常引入信号序列和MHC导向序列,以提高多肽的呈递效率64-65
外源性RNA进入人体后会被Toll样受体(TLR)识别,产生IFN-α和TNF-α,并增加HLA-DR的表达,从而引发不依赖序列的基因抑制、细胞活化和促炎细胞因子的产生66。Drew Weissman和Katalin Karikó发现,mRNA经过m5C、m6A、m5U、s2U或假尿苷等核苷修饰后,明显降低了进入机体后诱导单核细胞来源的DC分泌TNF-α和IL-12以及表达CD80、CD83、CD86和HLA-DR的能力67。2023年诺贝尔生理学或医学奖颁发给了这两位科学家,以表彰他们在核苷碱基修饰领域的杰出发现。
在多肽/蛋白疫苗设计方面,引入非天然氨基酸(unnatural amino acid,UAA)可以克服自身免疫耐受,改进抗原的稳定性和免疫原性,从而提高机体对疫苗的免疫反应。通过引入非天然氨基酸α-甲基丝氨酸(α-methylserine)到MUC1蛋白的结构域后,该疫苗的多肽骨架能够呈现生物活性构象,比天然氨基酸更耐酶解,提高了抗原的稳定性68。引入对硝基苯丙氨酸[p-nitrophenylalanine,pNO(2)Phe]到自身蛋白质后,该抗原能触发强而持久的多克隆IgG抗体反应69
肿瘤抗原在人体内存在自身免疫耐受,通过对疫苗与新型蛋白或生物材料的改造,可以增加新抗原疫苗的免疫原性。乳腺癌T7-MUC1多肽疫苗结合了新型的Toll样受体7(TLR7)激动剂与MUC1多肽,能显著增强小鼠骨髓树突状细胞和脾脏淋巴细胞的细胞因子释放,并能诱导特异性杀伤反应。在体内实验中,T7-MUC1疫苗使小鼠4T1肿瘤的重量减少了超过70%,表现出明显的抗肿瘤效果70。NvIH是一种单剂量注射纳米疫苗和免疫检查点抑制剂水凝胶,共包封了ICB抗体与新型聚合物纳米颗粒,并负载了TLR7/8/9激动剂和干扰素基因刺激剂(STING)。原位肿瘤疫苗接种后,NvIH促使肿瘤滞留时间延长,维持免疫治疗释放,减少急性全身炎症。单剂量NvIH成功降低多个免疫原性较差的肿瘤模型的TME免疫抑制,调动强大的TME和系统先天适应性抗肿瘤免疫,实现了局部和远处大肿瘤的消退71
给药方式和递送系统对疫苗的免疫效果至关重要。传统疫苗常通过肌内或皮下注射,而新型研究利用纳米粒子、病毒载体或细胞膜包裹疫苗实现靶向给药,使新抗原专一富集在肿瘤细胞周围。一种选择性器官靶向(SORT)策略可以实现肝外mRNA的靶向递送,以肺、脾、肝为靶点的SORT脂质纳米粒子可用于编辑上皮细胞、内皮细胞、B细胞、T细胞和肝细胞72,并且首次实现了蛋白RNA复合物的器官靶向递送73。一种内源性脂质体纳米颗粒(LNP)113-O12B可以靶向输送淋巴结,包裹mRNA后,能增强对编码全长卵清蛋白(OVA)模型抗原的CD8+ T细胞反应。在携带OVA抗原的B16F10黑色素瘤模型中,编码OVAmRNA疫苗的疗效也得到改善。用113-O12B封装编码TRP-2肽的mRNA疫苗也具有明显的抑瘤作用74
病毒载体疫苗将肿瘤抗原导入病毒颗粒中,通过模拟病毒感染,激活抗原呈递细胞,使其更好地识别并呈递肿瘤相关抗原,促进CD8+ T细胞的活化。Talimogene laherparepvec(T-VEC)是一种源自1型单纯疱疹病毒的溶瘤免疫疗法,将其直接注射到黑色素瘤病灶中可造成肿瘤细胞的溶解,从而使肿瘤细胞破裂,并释放出肿瘤抗原和粒细胞巨噬细胞集落刺激因子(GM-CSF),加速抗肿瘤的免疫应答。一项随机对照Ⅲ期OPTiM临床研究入组了436位不可切除ⅢB、Ⅳ期黑色素瘤患者,295位患者接受T-Vec治疗,141位患者接受GM-CSF治疗。T-Vec组持续缓解率(DRR)显著高于GM-CSF组(16.3% vs. 2.1%;or 8.9;P<0.001),T-VEC最常见的不良反应(AE)是疲劳、寒战和发热75。基于该临床研究结果,FDA批准了T-Vec用于首次手术后复发的黑色素瘤患者不可切除的皮肤、皮下和淋巴结病灶的局部治疗。这也是首个获得FDA批准的溶瘤病毒类治疗药物。一种基于腺病毒(Ad)载体的肿瘤新抗原疫苗可以提高免疫原性和抗肿瘤效果。该病毒载体疫苗与抗PD-1联合治疗增加了新抗原特异性CD8+ T细胞的数量,引起肿瘤中效应性CD8+ T细胞的增加。12名错配修复不良(dMMR)的转移性肿瘤患者接种该腺病毒载体疫苗后,观察到了TCR库的扩张和TCR克隆多样性的增加,以及具有效应记忆特征的肿瘤浸润性T细胞的增加76
免疫佐剂是一类具有调节免疫反应功能的分子,通过模拟免疫系统中的“模式识别受体”(pattern recognition receptor,PRR)来激活免疫应答。理想情况下,肿瘤疫苗佐剂不仅能够保护抗原不被降解,还可以促进抗原呈递细胞高效摄取和呈递抗原,从而触发强大且持久的辅助性T细胞和细胞毒性T细胞免疫应答。免疫佐剂在激活先天免疫应答方面同样具有重要作用。PRR能够辨识病原体特定的分子模式,从而引发免疫系统的报警反应。因此,免疫佐剂可以提高疫苗所引入抗原的识别和攻击效果,提升疫苗的免疫原性,增强对抗原的免疫记忆77
常用的佐剂包括Poly-ICLC、GM-CSF、Montanide、IL-12等78-81。Poly-ICLC可模拟病毒感染,激活先天免疫和促进T细胞扩增。GM-CSF可刺激巨噬细胞和粒细胞,增强抗原处理和呈递能力。Montanide可增强细胞毒T细胞反应。这些佐剂与新抗原疫苗联合使用,可增强疫苗免疫原性。但单一佐剂可能无法提供所有效应,因此佐剂的合理选择和组合对优化疫苗至关重要82-84。同一部位重复接种(SSV)Montanide ISA-51+polyICLC±黑色素瘤肽疫苗后,对接种部位活检进行RNAseq基因表达分析发现,在27名患者的接种部位微环境(VSME)样本中,使用佐剂和肽的疫苗在CD80、CD83、CD86、CD40和CD40L的表达水平显著增强。含有polyICLC的疫苗增强了TBX21(T-bet)的表达,但GATA3没有降低,而使用佐剂和抗原肽进行SSV显著增强了TBX21的表达并降低了GATA3的表达。此外,佐剂和肽的同一部位重复接种还增强了与TLS形成相关趋化因子的表达85
为克服免疫逃逸对新抗原疫苗疗效的影响,联合治疗成为常见策略。最常见的是与免疫检查点抑制剂联合使用,如PD-1/PD-L1、CTLA-4抑制剂,可抑制关键免疫细胞表面受体与配体的结合。BNT111是一种静脉给药的治疗性脂质体RNA肿瘤疫苗,编码了4种TAA,Ⅰ期临床试验入组了免疫检查点抑制剂(CPI)治疗失败的晚期黑色素瘤患者,中期分析显示,BNT111诱导产生了PD+效应记忆表型T细胞应答,具有持久的客观缓解,联合PD-1单抗组ORR达到了35.2%,而单用BNT111组ORR仅为16.6%86。有研究构建了一种基于纳米技术的mRNA疫苗,通过纳米颗粒将编码肿瘤抗原MUC1的mRNA传递到淋巴结中的树突状细胞,激活和扩展肿瘤特异性T细胞。体内研究表明,基于纳米颗粒的mRNA疫苗在DC中成功表达肿瘤抗原,与疫苗或单克隆抗体单独使用相比,疫苗联合抗CTLA-4单抗显著增强抗肿瘤免疫反应87
此外,化疗、放疗和手术也是常见的联合策略。化疗可增加肿瘤中新抗原的表达,与新抗原疫苗联合可产生协同增效。肿瘤新抗原疫苗TG4010联合化疗不仅延长了晚期非小细胞肺癌患者的生存期,还诱导了针对其他肿瘤相关抗原的CD8+ T细胞反应的交叉反应即表位扩散现象88。在个性化新抗原疫苗NEO-PV-01一线联合治疗晚期非鳞状非小细胞肺癌(NSCLC)的Ⅰb期临床试验中,接受该疗法的38名患者没有发生与治疗相关的严重不良事件,除了检测到新抗原特异性CD4+ T细胞和CD8+ T细胞免疫应答,还引发了表位扩散到非接种新抗原,包括对KRAS G12C和G12V突变的反应89。脑瘤树突细胞疫苗DCVax-L与标准治疗联合应用,可显著提高恶性脑胶质瘤的治疗效果。在新诊断患者中,DCVax-L可使5年生存率提高1倍,降低20%死亡风险;在复发患者中,则可降低42%死亡风险。DCVax-L还激活肿瘤特异性T细胞,动员大规模、持久的免疫应答90
肿瘤新抗原疫苗在当前阶段面临诸多挑战。首先,某些肿瘤免疫原性低,难以被免疫系统识别和产生强有力的免疫反应,被称为“冷肿瘤”。另外,肿瘤细胞通过多种策略来规避免疫监视,如表位丢失和MHCⅠ分子的下调等机制,使肿瘤细胞在免疫系统面前变得“隐形”,逃避了免疫攻击91。同时,在缺氧、偏酸的肿瘤微环境中,多种免疫抑制细胞如肿瘤相关巨噬细胞(TAM)、调节性T细胞(Treg)和髓系抑制细胞(MDSC),也影响免疫系统的功能92。其中MDSC作为一类异质性的未成熟髓样细胞,是主要负调节因子之一,与肿瘤患者的预后和总体生存率呈负相关93
未来,寻找通用型肿瘤疫苗将成为肿瘤免疫治疗领域的一个重要方向。尽管个体化肿瘤新抗原疫苗在实现高度特异性方面取得了显著进展,但其制备周期长、生产成本高,限制了其广泛应用。通用型疫苗基于分析COSMIC、TCGA等数据库中最常见突变基因,选择不同肿瘤患者之间共同驱动的基因突变作为靶点。通过经过预测表位、验证、药物设计等步骤,通用型疫苗成为适用于多个驱动基因突变人群的疫苗产品。目前,一些常用的通用型疫苗已经涉及一些常见的基因突变,如IDH194-95、TP5396、KRAS97等。一项Ⅰ/Ⅱ期临床研究入组了KRAS突变肽通用型疫苗的免疫原性,在接种疫苗的5例胰腺癌患者中有2例出现了特异性T细胞免疫反应,中位生存期延长至10.5个月,无反应者为4.5个月98
总之,肿瘤新抗原疫苗的研究和应用已经取得了令人瞩目的进展,高通量测序和生物信息学技术为这一领域注入了新的活力。通过精准预测、验证数据库的建立、递送系统的不断改进,并与现有治疗方法联合使用,肿瘤疫苗有望在癌症治疗中发挥重要作用,为患者带来更为有效和个体化的治疗选择。
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2024年第5卷第2期
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doi: 10.12211/2096-8280.2023-060
  • 接收时间:2023-08-25
  • 首发时间:2025-07-07
  • 出版时间:2024-04-30
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  • 收稿日期:2023-08-25
  • 修回日期:2023-11-12
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    1 中国人民解放军总医院第一医学中心生物治疗科,北京 100039
    2 九江市第一人民医院肿瘤科,江西 九江 332000
    3 中国人民解放军总医院第五医学中心血液病医学部,造血干细胞治疗及转化研究北京市重点实验室,北京 100071

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张斌(1970—),男,研究员,博士生导师。研究方向为血干细胞移植、干细胞研究与转化应用、肿瘤免疫细胞治疗等。E-mail:
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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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