TME-NET:一种可解释的深度神经网络,用于预测泛癌症免疫检查点抑制剂反应
Xiaobao Ding1,2,3, Lin Zhang1, Ming Fan1
1Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou 310018, Zhejiang, China.
Briefings in bioinformatics
|August 21, 2024
概括
一个新的神经网络,TME-NET,通过分析瘤微环境 (TME),准确地预测患者对免疫检查点抑制剂 (ICI) 的反应. 该模型识别了影响治疗结果的关键免疫细胞和基因,为癌症免疫疗法提供了宝贵的见解.
科学领域:
- 计算生物学是一种计算生物学.
- 癌症免疫学 癌症免疫学
- 医学中的人工智能.
背景情况:
- 免疫检查点抑制剂 (ICI) 是重要的癌症疗法,但预测患者的反应仍然是一个挑战.
- 瘤微环境 (TME) 显著影响ICI疗效,但全面的TME评估往往被忽视.
- 现有的预测模型可能无法完全捕捉复杂的TME在免疫治疗结果中的作用.
研究的目的:
- 开发和验证一种新的神经网络模型,TME-NET,用于预测患者对ICI的反应.
- 综合分析TME的分层结构,以确定影响免疫治疗的关键组件.
- 为推动TME介导免疫反应的机制提供可解释的见解,并预测患者的存活率.
主要方法:
- 定义了一个层次的TME结构 (结果,免疫作用,细胞,细胞组成部分,基因).
- 一个神经网络 (TME-NET) 被开发和训练在一个癌队列的948名患者在四种癌症类型.
- 通过参数重量可视化和细胞剥离研究来实现模型解释性;使用AUC和精度来评估性能.
主要成果:
- 与SVM和k-NN.等传统模型相比,TME-NET显示出更好的预测性能 (AUC和准确性).
- 关键发现揭示Th1细胞促进抗瘤免疫力,而M2巨细胞和髓状细胞衍生的抑制细胞表现出免疫抑制作用.
- 特定的基因 (STAT4,IRF4,IFNG,ARG1) 和细胞类型被确定为TME动态和患者存活率 (p < 0.01) 的关键调节器.
结论:
- TME-NET有效地预测免疫疗法反应,并提供对TME组成和功能的可解释的见解.
- 该模型强调了特定免疫细胞子集和基因表达在确定治疗成功中的关键作用.
- TME-NET为个性化癌症治疗策略提供了一个强大的工具,可以通过https://immbal.shinyapps.io/TME-NET.NET访问.
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