免疫检查点抑制剂治疗后长期存活益处的多omics指标
Jie Zhao1, Yiting Dong1, Hua Bai1
1CAMS Key Laboratory of Translational Research on Lung Cancer, State Key Laboratory of Molecular Oncology, Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100021, China.
Cell reports methods
|September 22, 2023
概括
研究人员在免疫检查点抑制剂 (ICI) 治疗后开发了一种长期存活 (LTS) 的预测模型. 该模型使用多omics数据识别关键生物标志物,并优于黑色素瘤和肺癌患者的个体预测因素.
科学领域:
- 在瘤学瘤学.
- 免疫治疗是一种免疫疗法.
- 生物信息学是一种生物信息学.
背景情况:
- 在免疫检查点抑制剂 (ICI) 治疗后预测长期存活率 (LTS) 对于优化癌症治疗至关重要.
- 现有的ICI反应预测标记通常缺乏对长期结果的全面洞察力.
研究的目的:
- 开发和验证ICI治疗后LTS的预测模型.
- 确定可以作为可靠的生物标志物用于免疫治疗的长期反应的分子指标.
主要方法:
- 利用了来自158项临床试验的数据 (25种癌症类型的21,023名患者) 与1年的整体存活率.
- 作为LTS的替代品,使用TCGA多omics数据集成的1年OS率.
- 采用高斯过程回归来构建"免疫治疗的长期生存预测得分" (iLSPS) 模型.
主要成果:
- 确定了总新抗原,新陈代谢得分,CD8+T细胞计数和MHC得分作为预测生物标志物.
- 与单个生物标志物相比,iLSPS模型表现出优异的预测性能.
- 在黑色素瘤和肺癌患者队列中成功预测了LTS.
结论:
- 一年OS率可以作为评估ICI治疗中长期存活率的有效替代品.
- 通过机器学习集成的多omics数据可以有效地建模和预测长期生存结果.
- 开发的iLSPS为预测免疫治疗反应和指导临床决策提供了一个有前途的工具.
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