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个性化肿瘤新抗原疫苗中抗原肽预测研究进展

  • 王广志 1

    机 构： 上海海洋大学食品学院，上海 201306

    ×
  • 李雨雨 1

    机 构： 上海海洋大学食品学院，上海 201306

    ×
  • 谢鹭 1,2

    机 构：

    1. 上海海洋大学食品学院，上海 201306

    2. 上海生物信息技术研究中心，上海 201203

    ×

1. 上海海洋大学食品学院，上海 201306 ； 2. 上海生物信息技术研究中心，上海 201203

中图分类号: Q7,Q81

DOI: 10.16476/j.pibb.2019.0019

Advances in The Prediction of Antigenic Peptides in Personalized Tumor Neoantigen Vaccine

  • WANG Guang-Zhi 1

    Affiliation： College of Food Science, Shanghai Ocean University, Shanghai 201306, China

    ×
  • Yu-Yu 1

Affiliation： College of Food Science, Shanghai Ocean University, Shanghai 201306, China

×
  • ） XIE Lu 1,2

    Affiliation：

    1. College of Food Science, Shanghai Ocean University, Shanghai 201306, China

    2. Shanghai Center for Bioinformation Technology, Shanghai 201203, China

    ×

    • 1. College of Food Science, Shanghai Ocean University, Shanghai 201306, China ； 2. Shanghai Center for Bioinformation Technology, Shanghai 201203, China

    Q7,Q81

    DOI: 10.16476/j.pibb.2019.0019

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    目录 contents

      摘要

      失控的突变导致肿瘤的发生，其中某些非同义突变（错义、移码、融合）多肽，被蛋白酶体降解成短肽后被抗原提呈细胞（antigen-presenting cells，APCs)识别，呈递至引流淋巴结，符合主要组织相容性复合物(major histocompatibility complex，MHC)结合基序的短肽，继而被T细胞表面因子捕获而产生免疫反应，引发肿瘤的消退，我们称之为“新抗原”（neoantigens）. 这类抗原由于未受胸腺的阴性筛选，被T细胞识别为“异类”，不易受免疫耐受机制的影响，从而可作为免疫介导肿瘤治疗的有效靶点. 新一代测序技术极大推动了新抗原疫苗的可行性，但从测序识别肿瘤的体细胞突变到TCR(T-cell receptor)识别新抗原产生免疫反应，中间存在着大量的候选假阳性新抗原多肽，这对于针对新抗原而设计疫苗无疑是难以跨越的障碍. 一套有效合理的筛选方法，是新抗原疫苗制备过程中不可或缺的一环. 然而国内未见相关综述报道，本文调研了目前新抗原免疫治疗过程中的新抗原肽预测及筛选研究进展.

      Abstract

      Runaway mutations cause tumors, some of the non-synonymous mutational (i.e., missense, frameshift, fusion) peptides are degraded into short peptides by proteasome, then APCs (antigen-presenting cells) recognize and present them to draining lymph nodes. These short peptides, in line with the combination of MHC (major histocompatibility complex) motif, are captured by the T cell surface factors and produce an immune response, eventually lead to tumor regression. We call these peptides neoantigens, because these antigens are not negatively screened by thymus gland, they are recognized as "alien" by T cells and are not susceptible to immune tolerance mechanism. Neoantigens can act as effective targets for immune-mediated tumor control. The next generation sequencing technology greatly boosts the feasibility of neoantigen vaccine design, however identifying tumor somatic mutations that can be presented and recognized by TCR (T-cell receptor) is a very demanding yet key step. Many of the predicted "positive" neoantigen peptides are actually "false" and need to be eliminated in further steps to clinical application. Therefore effective screening method is an indispensable part of neoantigen vaccine therapy. However, no related reports have been seen in domestic. In this paper, the computational prediction process of screening methods for potential neoantigen peptides are reviewed.

      疫苗因能够激活免疫系统以预防或治疗感染和一些其他疾病而对人类健康有着重要的影响 ，然而预防性的癌症疫苗目前仅对病毒来源的癌症有效，如人类乳头瘤病毒介导的宫颈 癌 [ 1 , 2 ] . Sipuleucel-T疫苗是美国食品药品管理局（FDA）首个批准的治疗性的癌症疫苗，对前列腺癌的治疗也有一定的效 果 [ 3 ] . 与其他的免疫疗法，如免疫检查点抑制（immune checkpoint blockade，CPB）、过继T细胞疗法（adoptive cell transfer，ACT）以及抗原嵌合受体疗法（chimeric antigen receptors，CARs）相比，大多数疫苗都不能产生显著的临床效 果 [ 4 ] . 新抗原疫苗是最近开发出的一种有效的癌症疫苗，也是目前最前沿的抗肿瘤疫苗技术之一，接种新抗原疫苗既可以扩大已有的新抗原特异性T细胞群，又可以在癌症患者中诱导更广泛的新型T细胞特异性，增强肿瘤抑 制 [ 5 ] ，同时新抗原疫苗与其他免疫疗法如PD-1/PD-L1联合治疗效果更显 著 [ 6 , 7 ] .

      传统上，癌症疫苗被设计针对肿瘤中那些过表达的肿瘤相关抗原（tumor associated antigens，TAAs）或胚胎抗原（carcinoembryonic antigen，CTAs），然而TAAs由于来源于人体正常组织，经过胸腺发育的阴性选择，因而可能引发中央和外周免疫耐受机制，从而导致疫苗接种的低效率以及产生自体免 疫 [ 8 ] . 肿瘤特异性抗原（tumor-specific antigen，TSAs）又称“新抗原”，是由肿瘤内部随机的体细胞突变产生的，不同于正常组 织 [ 9 , 10 ] ，可以引发有效、安全和高度特异性的抗肿瘤免疫反应. 2017年，Ot t [ 11 ] 和Sahi n [ 12 ] 团队均证实，筛选来自黑色素瘤中的“新抗原”制成的多肽以及RNA疫苗，均可刺激CD 8 + 以及CD 4 + T细胞的增殖从而导致肿瘤的消退. 值得注意的是，本来针对CD 8 + T细胞设计的疫苗，却介导了更多的CD 4 + T细胞的增多，这引发了人们对主要组织相容性复合物MHC-Ⅱ类分子抗肿瘤作用的思考. 个性化肿瘤新抗原疫苗正受到越来越多肿瘤患者的关注. 虽然个性化新抗原疫苗目前被认为是一种极其有效且安全的免疫治疗，但是其制备复杂耗时、专业性高，尤其是疫苗制备的第一步新抗原的筛选尤其困难. 本文将就目前的新抗原多肽筛选的研究方法进行综述，为相关领域的研究者和应用者们提供参考.

    • 1　抗原的提呈

      新抗原产生于自体，属于内源性抗原亦称肿瘤特异性抗原（TSAs）. 准确理解抗原的提呈对精准预测新抗原是至关重要的. 抗原的呈递是抗原识别中最具选择性的一步，新抗原亦不例外，抗原多肽只有高亲和结合到MHC分子继而被提呈到肿瘤细胞的表面，T细胞才有可能与之接触识别. 抗原处理和提呈的步骤包括：内源性蛋白质被多催化蛋白酶体降解成肽段群，在胞浆中被其他蛋白酶进一步修剪，其中一部分被抗原处理复合体转运子（transporter associated with antigen processing，TAP）选择性地转运到内质网，在那里被内质网氨基肽酶进一步修剪. 最后肽段在一系列蛋白质集合下被绑定到MHC分子，MHC-肽复合体最终由高尔基体输出至肿瘤细胞表面，经TCR判断是否开启免疫应 答 [ 13 ] .

    • 2　新抗原肽提呈过程研究中常用的生物信息学软件

      洗脱实验发现，无论Ⅰ类还是Ⅱ类MHC分子，都有固定的抗原肽氨基酸残基序列——称为“common motif”，即锚定位点. MHC分子在人类中被叫做人白细胞抗原(human leucocyte antigen,HLA),主要向CD 4 + T细胞提呈外源性抗原，其肽结合凹槽两端呈开放状态，能够容纳约10~30个较长肽段氨基酸残基，同时有着更复杂的结合基序. 由于新抗原是内源性的，且MHC-Ⅰ分子结合的多肽长度比较固定，常为8~11肽，加上基序较之MHC-Ⅱ分子相对简单，目前的研究大都集中在MHC-Ⅰ分子提呈多肽的预测. 但随着新抗原疫苗临床实验证实很多的CD 4 + T细胞被同样介导而增殖，人们也开始越来越关注MHC-Ⅱ分子的提呈作用. 基于这些锚定位点信息，结合机器学习算法，国际上已开发出多种预测表位肽与MHC结合的软件工 具 [ 14 , 15 ] ，常用的MHC-肽结合工具见表 1 .

      表1 MHC-肽结合预测工具

      Table 1 MHC binding affinity prediction tools

      软件/模型 发表/更新时间 pMHC-Ⅰ类结合 pMHC-Ⅱ类结合 预测算法
      NetMH C [ 16 ] 2008.08. √ 神经网络
      PSSMHCpa n [ 17 ] 2017.05. √ 位置特异性打分矩阵
      NetMHCpa n [ 18 ] 2017.10. √ 神经网络
      MHCflurr y [ 19 ] 2018.07. √ 神经网络
      PROPRE D [ 20 ] 2001.12. √ 打分矩阵
      CONSENSU S [ 21 ] 2010.11. √ 打分矩阵
      NetMHCIIpa n [ 22 ] 2018.02. √ 神经网络
      BOT A [ 23 ] 2018.10. √ 深度神经网络

      NetMHCpan-4.0就是利用人工神经网络（ANN）预测肽与已知序列的MHC-Ⅰ分子结合，该方法在超过180 000个定量结合数据和MS衍生的MHC洗脱配体的组合上进行训练. 结合亲和力数据涵盖来自人（HLA-A，B，C，E）、小鼠（H-2）、牛（BoLA）、灵长类动物（Patr，Mamu，Gogo）和猪（SLA）的172个 MHC分子. MS洗脱的配体数据涵盖55个HLA和小鼠等位基因，用NetMHCpan软件算法进行肿瘤新抗原预测，计算MHC-Ⅰ类分子与预测肽的结合力. 预测的结果中，若 %rank<0.5,认为该短肽是MHC-Ⅰ类分子的强结合；0.5<%rank<2，认为该短肽是MHC-Ⅰ类分子的弱结合；%rank>2，认为该肽不能与MHC-Ⅰ分子结合. 若被预测的肽段与MHC-Ⅰ类分子的结合力越强，其成为新抗原表位的可能性越大. NetMHCIIpan是典型的MHC-Ⅱ类分子结合肽预测工具，类似于NetMHCpan,可用于预测人的MHC-Ⅱ类HLA-DR、HLA-DP和HLA-DQ，以及小鼠分子（H-2）结合肽的预测，阈值在2<%rank<10. 即便NetMHCpan是目前广泛认可的MHC-肽结合预测软件，其预测结果中依然存在着大量的假阳性候选新抗原多肽，仅仅约1%的预测结合肽能被TCR识别产生免疫原 性 [ 24 ] . 因为预测出的绑定多肽可能不被蛋白酶体水解产生，或者不能有效地刺激CD 8 + T. 此外免疫表位数据库（the immune epitope database，IEDB ） [ 25 ] 整合了目前比较常用的一些新抗原筛选的可用预测软件，涉及有蛋白酶体切割NetChop、TAP转运、MHC结合预测等. 相关的软件还有HLA亚型预测：例如HLAmine r [ 26 ] 、Athlate s [ 27 ] 、Polysolve r [ 28 ] 、Optityp e [ 29 ] 、seq2HL A [ 30 ] 等. 基于WGS/WES测序数据预测患者的HLA分型，其预测精度可达90%~99%不等.

    • 3　新抗原肽筛选流程及常用的管道（pipeline）

    • 3.1　基于MHC-多肽结合预测的新抗原筛选流程

      目前的个性化新抗原多肽筛选流程总体可分为4步：a. 对病人外周血单核细胞和自身肿瘤组织进行全外显子组（whole exome sequencing，WES）测序找出肿瘤特异性突变多肽，利用外周血细胞RNA-seq或者DNA-seq分析患者HLA分型. b. MHC-肽结合预测软件预测MHC呈递的多肽. c. 进一步筛选可信度高的新抗原，降低阳性肽条目. d. 人工合成新抗原多肽，进行体外T细胞识别验证实验. 常规的新抗原预测流程见图 1 .

      图1 基于MHC-多肽结合预测的新抗原筛选流程示意图

      图1 基于MHC-多肽结合预测的新抗原筛选流程示意图

      Fig. 1 Flow chart of neoantigen prediction base on MHC binding affinity prediction

      准确鉴定患者的新抗原是困难的，目前主流的新生抗原预测的主要依赖体细胞错义突变来源的多肽，例如SNV和indel，由于这些方法没有考虑当两个基因在基因组中重排异常转录产生的融合基因，相关研究者也开发了关于融合基因的新抗原预测流程INTEGRATE-neo，详细的基于MHC-肽结合预测的新抗原筛选管道见表 2 .

      表2 新抗原预测管道

      Table 2 Pipelines for neoantigen prediction

      软件 发表时间 突变来源 特点 HLA分型预测 MHC-多肽结合预测
      pVAC-se q [ 31 ] 2016.12. SNV，indel 整合肿瘤突变和表达数据 HLAminer/Athlates NetMHC3.4
      TSNA D [ 32 ] 2017.04. SNV，indel 增加了膜蛋白的胞外突变 SOAP-HLA2.2 NetMHCpan2.8
      Cloud-Ne o [ 33 ] 2017.06. SNV，indel 首款云计算工作流管道 HLAminer/Polysolver NetMHCpan3.0
      TImine r [ 34 ] 2017.10. SNV，indel 免疫浸润+新抗原分析 Optitype NetMHCpan2.8
      Neopepse e [ 35 ] 2018.04. SNV，indel 基于机器学习 - Netmhcpan
      INTEGRATE-ne o [ 36 ] 2016.10. SV 从融合基因中预测新抗原 HLAminer NetMHC3.4

      pVAC-seq，通过整合肿瘤突变和表达数据(DNA-seq和RNA-seq)识别特异性肿瘤突变. 开发者通过对4例转移的黑色素瘤患者使用pVAC-seq预测管道最终鉴定到能够引起免疫原性的新抗原数目分别为：3/16（19%）、3/14（21%）、3/18（17%）、4/12（33%）. TSNAD是国内浙江大学整合开发的一款预测新抗原管道，预测的突变不仅限于MHC分子提呈的多肽，而且增加了膜蛋白的胞外突变特性. Cloud-Neo是首款云计算工作流预测新抗原的管道，对23例黑色素瘤样本分析预测到的新抗原平均数量分别为：HLAminer，107.89；Polysolver，133.53；TIminer，结合RNA-seq数据使用Kallisto定量筛选确定表达的基因，预测新抗原. Neopepsee，通过输入RNA-seq原始数据以及体细胞突变VCF文件，运用机器学习的方法从14个新抗原相关特征中挑选9个强相关的特征预测新抗原. 相比于传统基于 IC 50/%rank值的预测方法，PR曲线（以查准率为纵轴，查全率为横轴绘制的曲线，曲线下面积越大性能越好）性能改进了2~3倍. 在224个公开的胃腺癌数据集上应用Neopepsee，平均每个病人预测到7个新抗原，且预测的新抗原负荷与患者的预后显著关联. INTEGRATE-neo，从融合基因出发预测新抗原. 目前新抗原预测的pipeline “核心”基本一致，均定位于外显子区域确定有表达的蛋白质.

    • 3.2　基于MHC结合肽的质谱新抗原筛选

      基于HLA结合肽免疫纯化的质谱数据新抗原预测，能极大提高预测准确 率 [ 37 , 38 , 39 ] ，因为基于质谱(MS)的方法可以精准捕获大量已加工肽和已呈递肽的描述，从而避免了抗原呈递预测所导致的误差. 基于质谱策略的新抗原筛选步骤主要包括：a. HLA结合肽的免疫纯化；b. 液相串联质谱鉴定（LC-MS/MS）；c. 用户自定义蛋白质数据库构建（SNP-calling突变蛋白序列以及相应的Uniport人类标准蛋白质序列）；d. 使用蛋白质组的定性定量分析工具，如Peak s [ 40 ] 、MaxQuan t [ 41 ] 、Masco t [ 42 ] 、SEQUES T [ 43 ] ，对MS数据进行搜库来鉴定真实MHC结合的突变多肽；e. 筛选候选新抗原，体外验证免疫原性.

      目前质谱策略存在的一个困难是患者多个HLA等位基因的共同表达使得鉴定到的多肽难以区分HLA亚型. 幸运的是， Wu研究小 组 [ 38 ] 通过使用表达单个HLA分型的细胞系很好地解决了这一难题，并用自开发的MSIntrinsicEC神经网络算法基于质谱多肽数据联合基因的表达量筛选，其预测到的新抗原能够达到50%以上，被认为是新抗原预测的极佳流程. 其研究于2017年发表在 Immunity ，但相关的算法并未公开. 最近Bulik-Sullivan 等 [ 39 ] 利用74例肿瘤患者HLA质谱数据构建的深度学习模型EDGE同样取得了惊人的效果，使用该模型分析9例经过PD-1 或 PD-L1治疗的转移性非小细胞肺癌患者，取预测排名前20的新抗原与T细胞体外培养2 w，结果5/9（56%）患者识别到新抗原特异性T细胞反应.

    • 4　新抗原特征及对预测新抗原进一步筛选的依据

      TSAs存在与自体多肽的差异，与TAAs受到中枢耐受机制的影响不同，因而T细胞可以很好地识别，但在其呈递过程中又受到多种因素的影响，导致预测结果较不理想，为了能够筛选到能真正具备激活T细胞免疫潜力的新抗原，研究人员在MHC-多肽结合性预测的基础上又做了许多筛选，如：与自体多肽的差异比 较 [ 44 ] 、与感染抗原的相似性比 较 [ 45 ] 、基于基因表达量的筛 选 [ 38 ] 、考察突变是否位于驱动性致癌基 因 [ 46 ] 等. 此外基于T细胞更可能作用于抗原肽的非锚定区域，如9肽的第3~7 位 [ 47 ] ，因此非锚定位点的突变被认为更能促进T细胞的识别. 另有关于20 000例黑色素瘤中新抗原的研究发现，98%的新抗原被发现在非驱动突变，仅有2%的新抗原来源于驱动突 变 [ 48 ] . 同时新抗原也很少存在共享的情况，仅仅H3.3 K27M、MYD88 L265P、KRAS G12D 在很少的患者中被同时发 现 [ 49 ] . 目前对TCR识别这一过程的机制还不明朗，所以即使是被MHC提呈的多肽，相当多的肽依旧不能引起免疫应答. 因此基于新抗原疫苗原理的免疫疗法也只能是个性化的.

    • 5　“非编码区筛选新抗原”——新的希望

      最近蒙特利尔大学免疫学和癌症研究所(IRIC)报道了他们的惊人发 现 [ 50 ] ：传统的基于WES的筛选忽略了很多TSAs，那些来自非编码区的未突变但异常表达的TSA（aberrantly expressed TSAs，aeTSAs）也应该被考虑是新抗原. 该团队建立了一种蛋白质基因组学的方法通过转录组测序以及质谱筛选从2个小鼠癌细胞系和7个人类原发性肿瘤中，鉴定到40个aeTSAs，发现其中90%的TSAs来自于非编码区，相关的研究也证实相对于突变的TSA（mutated tumor-specific antigens，mTSAs）的个体特异性，aeTSAs可以在肿瘤之间共享，因而aeTSAs是制备疫苗的首选. 同时99%的癌症突变来源于非编码区 域 [ 51 ] ，外显子区域只占人类蛋白质编码序列的2%，而高达75%的基因组是能够被潜在转录和翻译的. 这有可能间接解释了为什么目前基于WES新抗原预测的真阳性率那么低，不过尽管这一发现堪称领域的突破，它还需要更多的实验研究来证实. 如果这种情况广泛存在，相信当研究人员把基因非编码区也纳入新抗原预测范围，普适的新抗原疫苗将不再是梦想.

    • 6　目前新抗原预测存在的问题

      理想情况下产生的新抗原是能够被CD 8 + T细胞识别并清除的，然而肿瘤能够通过激活各种检查点机制来抑制这种免疫反应. 目前新抗原预测存在的问题主要有：a. 无法百分百的预测真实多肽呈递过程（质谱检测可以很好地改善这一问题）. b. 新抗原肽引发免疫原性的机制不明，我们需要更多的实验研究去发掘免疫肽的特性. c. 人类相关新抗原数据量目前还太少. d. 新抗原的个体特异性. e. 基于非编码区表达异常产生的新抗原预测流程还不够完善. f. 目前新抗原预测中未知因素高达45%，其他已知因素如MHC多肽复合物之间的亲和力因素占28%、基因的表达因素占18%、蛋白酶体的切割约占8%，意味着穷极目前的已有知识对于新抗原的预测结果也达不到60%. g. 存在一种可能，TCR识别是一个随机的过程，如果那样的话，从肿瘤中透视的信息可能永远无法精准地预测新抗原.

    • 7　展望

      虽然目前国内新抗原疫苗的发展较之国外还有很大的不足，也未见新抗原疫苗治疗取得成功的报道，但国内越来越多的学者开始关注新抗原，许多临床的实验也都在积极准备进行之中. 相信不久的将来，个性化肿瘤新抗原疫苗，会给肿瘤治疗带来新的希望. 同时通过对TCR的测序以及非编码区新抗原的揭示，新抗原治疗的边界和可应用性可能进一步拓展，真正成为肿瘤免疫治疗的重要范畴.

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  • 基本信息

    DOI: 10.16476/j.pibb.2019.0019

    中图分类号: Q7,Q81

    引用信息

    王广志,李雨雨,谢鹭.个性化肿瘤新抗原疫苗中抗原肽预测研究进展[J].生物化学与生物物理进展,2019,46(05):441-448.

    WANG Guang-Zhi,） LI Yu-Yu,） XIE Lu.Advances in The Prediction of Antigenic Peptides in Personalized Tumor Neoantigen Vaccine[J].Progress in Biochemistry and Biophysics,2019,46(05):441-448.

    基金信息

    国家自然科学基金资助项目（31870829）资助.

    This work was supported by a grant from The National Natural Science Foundation of China (31870829).

    稿件历史

    : 2019-05-20
    : 2019-01-25
    : 2019-04-01

    • 王广志

    机 构： 上海海洋大学食品学院，上海 201306

    Affiliation： College of Food Science, Shanghai Ocean University, Shanghai 201306, China

    李雨雨

    机 构： 上海海洋大学食品学院，上海 201306

    Affiliation： College of Food Science, Shanghai Ocean University, Shanghai 201306, China

    谢鹭

    机 构：

    1. 上海海洋大学食品学院，上海 201306

    2. 上海生物信息技术研究中心，上海 201203

    Affiliation：

    1. College of Food Science, Shanghai Ocean University, Shanghai 201306, China

    2. Shanghai Center for Bioinformation Technology, Shanghai 201203, China

    软件/模型 发表/更新时间 pMHC-Ⅰ类结合 pMHC-Ⅱ类结合 预测算法
    NetMH C [ 16 ] 2008.08. √ 神经网络
    PSSMHCpa n [ 17 ] 2017.05. √ 位置特异性打分矩阵
    NetMHCpa n [ 18 ] 2017.10. √ 神经网络
    MHCflurr y [ 19 ] 2018.07. √ 神经网络
    PROPRE D [ 20 ] 2001.12. √ 打分矩阵
    CONSENSU S [ 21 ] 2010.11. √ 打分矩阵
    NetMHCIIpa n [ 22 ] 2018.02. √ 神经网络
    BOT A [ 23 ] 2018.10. √ 深度神经网络
    html/pibbcn/20190019/alternativeImage/c7a81ab1-827b-4fd2-9061-f0c23faec7de-F001.jpg
    软件 发表时间 突变来源 特点 HLA分型预测 MHC-多肽结合预测
    pVAC-se q [ 31 ] 2016.12. SNV，indel 整合肿瘤突变和表达数据 HLAminer/Athlates NetMHC3.4
    TSNA D [ 32 ] 2017.04. SNV，indel 增加了膜蛋白的胞外突变 SOAP-HLA2.2 NetMHCpan2.8
    Cloud-Ne o [ 33 ] 2017.06. SNV，indel 首款云计算工作流管道 HLAminer/Polysolver NetMHCpan3.0
    TImine r [ 34 ] 2017.10. SNV，indel 免疫浸润+新抗原分析 Optitype NetMHCpan2.8
    Neopepse e [ 35 ] 2018.04. SNV，indel 基于机器学习 - Netmhcpan
    INTEGRATE-ne o [ 36 ] 2016.10. SV 从融合基因中预测新抗原 HLAminer NetMHC3.4

    表1 MHC-肽结合预测工具

    Table 1 MHC binding affinity prediction tools 基于MHC-多肽结合预测的新抗原筛选流程示意图

    Fig. 1 Flow chart of neoantigen prediction base on MHC binding affinity prediction 新抗原预测管道

    Table 2 Pipelines for neoantigen prediction

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