近几十年来,治疗性癌症疫苗被认为是能够导致肿瘤消退的重要免疫治疗策略。在这些疫苗的开发中,新表位的鉴定起着关键作用,和不同的计算方法已经被提出和采用来指导和加速这一过程。在这种情况下,这篇综述确定并系统分析了最近发表在文献中的关于治疗性疫苗开发表位的计算预测的研究,概述关键步骤,以及相关程序的优势和局限性。在PRISMA扩展(PRISMA-ScR)之后进行了范围审查。在数据库中进行搜索(Scopus,PubMed,WebofScience,科学直接)使用关键词:新表位,表位,疫苗,预测,算法,癌症,和肿瘤。对2012年至2024年发表的49篇文章进行了综合分析。大多数已确定的研究集中在预测对实体瘤中的MHCI分子具有亲和力的表位,如肺癌。预测具有II类MHC亲和力的表位的开发相对不足。除了从高通量测序数据中预测新表位外,确定了其他步骤,例如新表位的优先排序和验证。Mutect2是最常用的变体调用工具,而NetMHCpan有利于新表位预测。人工/卷积神经网络是用于新表位预测的优选方法。为了优先考虑免疫原性表位,随机森林算法是最常用的分类算法。与新表位预测和优先级排序的计算模型相关的性能值很高;然而,大部分研究仍使用微生物组数据库进行培训。在55%的分析研究中验证了预测的新表位的体外/体内验证。确定了导致肿瘤成功缓解的临床试验,强调这种免疫治疗方法可以使这些患者受益。整合高通量测序,复杂的生物信息学工具,和严格的验证方法,通过体外/体内试验以及临床试验,基于肿瘤新表位的疫苗方法有望开发针对特定肿瘤癌症的个性化治疗性疫苗.
Therapeutic cancer vaccines have been considered in recent decades as important immunotherapeutic strategies capable of leading to tumor regression. In the development of these vaccines, the identification of neoepitopes plays a critical role, and different computational methods have been proposed and employed to direct and accelerate this process. In this context, this review identified and systematically analyzed the most recent studies published in the literature on the computational prediction of epitopes for the development of therapeutic vaccines, outlining critical steps, along with the associated program\'s strengths and limitations. A scoping review was conducted following the PRISMA extension (PRISMA-ScR). Searches were performed in databases (Scopus, PubMed, Web of Science, Science Direct) using the keywords: neoepitope, epitope, vaccine, prediction, algorithm, cancer, and tumor. Forty-nine articles published from 2012 to 2024 were synthesized and analyzed. Most of the identified studies focus on the prediction of epitopes with an affinity for MHC I molecules in solid tumors, such as lung carcinoma. Predicting epitopes with class II MHC affinity has been relatively underexplored. Besides neoepitope prediction from high-throughput sequencing data, additional steps were identified, such as the prioritization of neoepitopes and validation. Mutect2 is the most used tool for variant calling, while NetMHCpan is favored for neoepitope prediction. Artificial/convolutional neural networks are the preferred methods for neoepitope prediction. For prioritizing immunogenic epitopes, the random forest algorithm is the most used for classification. The performance values related to the computational models for the prediction and prioritization of neoepitopes are high; however, a large part of the studies still use microbiome databases for training. The in vitro/in vivo validations of the predicted neoepitopes were verified in 55% of the analyzed studies. Clinical trials that led to successful tumor remission were identified, highlighting that this immunotherapeutic approach can benefit these patients. Integrating high-throughput sequencing, sophisticated bioinformatics tools, and rigorous validation methods through in vitro/in vivo assays as well as clinical trials, the tumor neoepitope-based vaccine approach holds promise for developing personalized therapeutic vaccines that target specific tumor cancers.