使用瘤反应性TIL TCR-pMHC三元复合体识别有效的抑制瘤的新抗原
Sang Hoon Kim1, Bo Ryeong Lee1, Sung-Min Kim1
1Geninus Inc., Seoul, 05836, Korea.
Experimental & molecular medicine
|June 12, 2024
概括
我们开发了新的计算工具VACINUSpMHC和VACINUSTCR,以更好地预测哪些新抗原可以触发癌症免疫治疗的免疫反应. 这些工具改善了免疫新抗原的识别,提高了治疗潜力.
科学领域:
- 免疫学 免疫学 免疫学
- 生物信息学是一种生物信息学.
- 在瘤学瘤学.
背景情况:
- 新抗原是癌症免疫治疗的关键标,因为它们的瘤特异性表达.
- 目前的方法难以预测哪些新抗原是真正的免疫原,从而限制了治疗的有效性.
- 下一代测序和生物信息学已经推进了新抗原识别,但没有免疫性预测.
研究的目的:
- 开发和验证用于预测免疫性新抗原的in silico工作流程.
- 为了提高癌症免疫治疗中新抗原选择的准确性.
- 整合-MHC结合和T细胞受体相互作用,以提高预测.
主要方法:
- 开发了 VACINUSP MHC 工作流程,包括-MHC 结合.
- 开发了VACINUSTCR工作流程,使用深度学习集成T细胞受体-pMHC相互作用.
- 在肝细胞癌患者和小鼠黑色素瘤模型中验证的工作流程.
主要成果:
- 在8名HCC患者中验证了VACINUSP MHC和VACINUSTCR工作流程.
- VACINUSTCR将118种VACINUSPMHC候选物精制为48种,其中13种已证实在体外具有免疫性.
- 在体内研究表明预测的新表位素诱导T细胞反应,瘤贩运和抑制.
结论:
- 整合TCR-pMHC三元复合体预测显著改善了免疫性新抗原的识别.
- VACINUSP MHC和VACINUSTCR为癌症免疫治疗选择新抗原提供了更精确的方法.
- 这种增强的预测策略对开发有效的基于新抗原的癌症疫苗充满了希望.
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