在转移性NSCLC中适应免疫治疗的机器学习驱动策略
Maliazurina B Saad1, Qasem Al-Tashi1, Lingzhi Hong1,2
1Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Nature communications
|July 24, 2025
概括
一个新的机器学习模型,A-STEP,预测,在高级非小细胞肺癌 (NSCLC) 免疫检查点抑制剂 (ICI) 中添加化疗对个体有益. 这种方法改善了超越PD-L1的治疗选择,提高了无进展生存率.
科学领域:
- 在瘤学瘤学.
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 免疫检查点抑制剂 (ICI) 提高了晚期非小细胞肺癌 (NSCLC) 的存活率.
- 目前在ICI单疗和组合化疗之间进行选择的指导是有限的.
- 像PD-L1这样的单个生物标志物不足以预测治疗反应.
研究的目的:
- 开发一种机器学习模型,预测在先进的NSCLC中将化疗添加到ICI中的个人益处.
- 使用临床基因组数据估计异质治疗效应.
- 为优化NSCLC免疫疗法选择提供前性指导.
主要方法:
- 开发了一个集成的机器学习模型,A-STEP (基于注意力的治疗效果预测评分).
- 利用了来自四个大型队列 (共2 220名患者) 的临床基因组数据.
- 使用28个基因组和6个临床特征来预测治疗效果,计算了益处得分.
主要成果:
- 与独立模型相比,A-STEP实现了最大的3个月进展风险降低,与独立模型相比,提高了13-23%的加权风险降低.
- 该模型建议对50%以上的患者进行治疗变化,主要偏好ICI-Chemo.
- 对外部队列的模拟显示,A-STEP引导治疗改善了2年无进展生存率 (ICI-Mono的HR=0.60,ICI-Chemo的HR=0.58).
- 关键的预测功能包括FBXW7,APC和PD-L1.
结论:
- 机器学习可以通过建模治疗异质性来解决NSCLC免疫疗法选择中的关键差距.
- 通过超越传统生物标志物限制,A-STEP使精准医学成为可能.
- 这种方法优化了高级NSCLC患者的治疗决策,改善了结果.
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