基于瘤新位基的疫苗:对当前预测计算策略的范围审查
Luiz Gustavo do Nascimento Rocha1,2, Paul Anderson Souza Guimarães1,2, Maria Gabriela Reis Carvalho1,2
1Biologia Computacional e Sistemas (BCS), Instituto Oswaldo Cruz (IOC), Fundação Oswaldo Cruz, Rio de Janeiro 21040-900, Brazil.
Vaccines
|August 29, 2024
概括
计算方法加速了用于治疗癌症疫苗的新表位的识别. 这篇评论分析了预测工具,突出了NetMHCpan和神经网络,并强调了针对瘤回归的个性化疫苗的承诺.
科学领域:
- 免疫学和生物信息学
- 计算型疫苗学 计算型疫苗学
- 在瘤学瘤学.
背景情况:
- 治疗性癌症疫苗的目的是通过免疫疗法诱导瘤回归.
- 识别特定的新表位物对于开发有效的癌症疫苗至关重要.
- 计算方法越来越多地被用于预测和加速新表位发现.
研究的目的:
- 系统地审查和分析用于治疗疫苗开发中的新表位预测的最新计算方法.
- 概述当前的新生代预测程序的关键步骤,优势和局限性.
- 评估计算预测的新表位的验证和临床翻译.
主要方法:
- 在PRISMA-ScR扩展后进行了范围审查.
- 在Scopus,PubMed,Web of Science和Science Direct中使用关键字进行了搜索:新皮图,皮图,疫苗,预测,算法,癌症,瘤.
- 综合和分析了2012年至2024年间发表的49篇文章.
主要成果:
- 大多数研究集中在预测固体瘤中与MHC I类分子结合的表位;MHC II类预测尚未得到充分研究.
- Mutect2 (变量调用) 和NetMHCpan (表位预测) 是常用的工具,人工/卷积神经网络是首选的预测方法.
- 随机森林算法是优先考虑免疫原性表位的;55%的研究包括体外/体内验证,一些临床试验显示瘤缓解.
结论:
- 计算式的新位预测显示出高性能,但仍然存在挑战,包括使用微生物群数据库进行培训.
- 将先进的生物信息学工具与严格的验证相结合,对于开发个性化的基于新表位素的治疗性癌症疫苗至关重要.
- 基于neoepitope的疫苗对个性化癌症免疫疗法具有显著的前景,具有临床益处和瘤回归的证明潜力.
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