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索尔:一个TMB异质性适应性优化模型预测使用克隆基因组特征在群组结构数据中的免疫治疗反应

Yixuan Wang¹, Yanfang Guan^2,3, Xin Lai²

¹Department of Biomedical Engineering, College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, 29 Jiangjun Avenue, Jiangning, Nanjing 211106, China.

Briefings in bioinformatics

|December 16, 2024

概括

瘤突变负担 (TMB) 是一个重要的癌症生物标志物,但它不能完全捕捉瘤的复杂性. 一个新的模型,THOR,整合了瘤克隆性和子组数据,以改善患者分层和免疫治疗的预后预测.

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:

免疫检查点抑制剂 (ICI) 越来越多地用于各种癌症.
瘤突变负担 (TMB) 是ICI反应的生物标志物,但其临床实用性受到瘤复杂性的限制.
现有的方法与小型免疫疗法试验队伍和复杂的瘤生物学作斗争.

研究的目的:

开发一种先进的生物标志物模型,克服TMB的局限性.
改善癌症免疫治疗的患者分层和预后预测.
整合瘤克隆性和子组动态,以提高预测能力.

主要方法:

介绍TMB异质性优化回归 (THOR) 模型.
索尔集成瘤克隆性,多样化的临床终点,和子组特定的动态.
使用跨子组的融合技术来进行强大的数据共享和解释.

主要成果:

模拟证实了THOR对于统计推断的优越参数估计.
在238名患者的队列中,THOR证明了患者分层的增强.
对19个子组2212名患者的分析表明,THOR显著改善了预后预测.

结论:

通过考虑瘤异质性和亚组变异,THOR提高了TMB的预测能力.
该模型在癌症免疫治疗中提供了改善的患者分层和预后准确性.
在利用复杂的免疫遗传数据为临床决策方面,THOR代表了重大进展.

关键词:

癌症免疫疗法免疫疗法终点集成终点集成按组结构的数据数据.处罚的融合战略被处罚预后生物标志物预后生物标志物瘤克隆异质性的异质性

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