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可通用的人工智能预测跨癌症和治疗方法的免疫治疗结果
medRxiv : the preprint server for health sciences
|May 19, 2025
概括
一个新的基础模型,指南针,准确地预测患者对不同癌症类型的癌症免疫治疗的反应. 该工具通过识别响应者和揭示耐药性机制来增强临床决策和试验设计.
科学领域:
- 在瘤学瘤学.
- 免疫学 免疫学 免疫学
- 计算生物学 计算生物学
背景情况:
- 免疫检查点抑制剂 (ICI) 是标准的癌症治疗,但反应率有限.
- 目前的生物标志物 (例如,瘤突变负担,PD-L1) 缺乏跨癌症和治疗方法的概括性.
- 由于复杂的瘤免疫相互作用,预测ICI反应具有挑战性.
研究的目的:
- 开发一个强大的,可解释的基础模型来预测免疫疗法反应.
- 为了捕捉可适应不同临床环境的共享免疫反应原则.
- 改善ICI患者分层和临床试验设计.
主要方法:
- 开发了Compass,这是一个使用概念瓶架构的基础模型.
- 将泛癌症的转录基因数据编码成 44 种生物基础的免疫概念.
- 在33种癌症类型的10,184种瘤中接受了培训,并在16个独立队列中得到了验证.
主要成果:
- 在七种癌症和六种ICI中,Compass显著优于22种基线方法.
- 精度 (8.5%),MCC (12.3%) 和AUC-PR (15.7%) 在最低限度的再培训下取得了改进.
- 证明了对未见的癌症类型和治疗方法的概括性.
- 指南针分层反应者显示总生存时间明显长 (HR=4.7,p<0.0001).
结论:
- "指南针"提供了一种可概括和可解释的方法来预测免疫疗法反应.
- 该模型为响应和抵抗途径 (例如,TGF-β信号传递,T细胞功能障碍) 提供了机械洞察力.
- 指南针支持临床决策,患者分层,并为未来的临床试验设计提供信息.
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