瘤微环境的免疫定型揭示了癌症个性化免疫疗法的分子异质性
Dongqiang Zeng1,2,3, Yunfang Yu4,5,6,7, Wenjun Qiu3
1Cancer Center, The Sixth Affiliated Hospital, School of Medicine, South China University of Technology, Foshan, 528000, China.
概括
一个新的机器学习工具,TMEclassifier,将癌症分类为免疫排他性,抑制性或激活子类型. 这种分类有助于预测患者的预后和个性化免疫治疗策略,以获得更好的癌症治疗结果.
科学领域:
- 在瘤学瘤学.
- 免疫学 免疫学 免疫学
- 生物信息学是一种生物信息学.
背景情况:
- 瘤微环境 (TME) 异质性影响癌症预后和治疗疗效.
- 目前用于TME表征的方法缺乏可访问性和高吞吐量能力.
- 了解TME亚型对于开发有效的癌症免疫疗法至关重要.
研究的目的:
- 开发和验证TMEclassifier,一种机器学习工具,用于将癌症分类为免疫排他性 (IE),免疫抑制性 (IS) 和免疫激活 (IA) 亚型.
- 研究每个TME亚型的独特细胞组成和临床影响.
- 评估TMEclassifier在指导个性化免疫疗法的潜力.
主要方法:
- 开发使用大量RNA测序数据的TMEclassifier,将患者样本分类为IE,IS和IA亚型.
- 使用体内小鼠模型验证TME亚型差异和免疫治疗反应.
- 使用单细胞RNA测序来分析TME细胞异质性.
- 在胃癌队列 (TIMES-001) 和其他多样化的队列中进行了前性验证.
主要成果:
- TMEclassifier成功地将癌症分层为IE (高层细胞,攻击性表型),IS (骨髓原抑制细胞,免疫抑制) 和IA (T细胞存在,良好的免疫疗法反应) 亚型.
- 在体内模型中证实了亚型特异性差异和免疫治疗反应.
- 向IL-1显示出克服IS亚型免疫抑制和增强免疫治疗疗效的潜力.
- 在多个癌症队列中验证了TMEclassifier的预测.
结论:
- 瘤微环境分类器 (TMEclassifier) 为定义瘤微环境亚型提供了一种可访问,高通量方法.
- 这种工具可以有效地根据TME成分对患者进行分层.
- TMEclassifier促进了个性化免疫治疗策略的指导,以提高治疗精度和克服耐药性.
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