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基于机器学习的开发,用于NK细胞治疗癌症的细胞毒性预测模型

Jie Ma¹, Jingjing Yue¹, Yangyang Li¹

¹Key Laboratory of Immune Response and Immunotherapy, the first affiliate hospital of USTC, Division of Life Sciences and Medicine, School of Basic Medical Sciences, University of Science and Technology of China, Hefei, Anhui, 230021, China.

Cellular oncology (Dordrecht, Netherlands)

|October 31, 2025

概括

一个新的机器学习模型通过分析受体-连接体相互作用来预测自然杀手 (NK) 细胞治疗的有效性. 该工具增强了针对个性化癌症治疗的抗瘤免疫预测.

科学领域:

免疫学免疫学免疫学
计算生物学计算生物学
在瘤学瘤学.

背景情况:

自然杀手 (NK) 细胞对于抗瘤免疫是至关重要的.
NK细胞疗法的疗效受到患者特异性受体-连接体表达变异性的限制.
预测NK细胞的治疗反应需要了解这些复杂的相互作用.

研究的目的:

开发NK细胞治疗结果的预测模型.
确定影响NK细胞细胞毒性的关键受体-连接体对.
利用NK细胞实现个性化的癌症治疗策略.

主要方法:

机器学习 (随机森林模型) 应用于受体-连接体表达数据.
流细胞测量用于剖析NK和瘤细胞相互作用.
模型在独立数据集和跨瘤类型上得到验证.

主要成果:

在预测卵巢癌NK细胞疗效时,获得了84.2%的准确性和0.908AUC.
证明了受体连接体表达和NK细胞死亡之间的复杂,非线性关系.
识别了癌症特异性的配体模式和转录基因数据集成的潜力.

结论:

开发的模型为用NK细胞精确治疗瘤提供了基础.
这种预测工具显示出在免疫治疗中临床应用的潜力.
促进了基于NK细胞的疗法改善患者选择和治疗规划.

关键词:

细胞毒性细胞毒性免疫治疗是一种免疫疗法.连接物连接物连接物自然杀手细胞是什么?预测模型是一个预测模型.接收器接收器接收器瘤是一个瘤.

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