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基于机器学习的预测建模最大化了mTOR/p53联合向治疗AML的有效性

Jingmei Li¹, Emi Sugimoto¹, Keita Yamamoto¹

¹Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan.

|August 11, 2025

概括

与TP53激活相结合的mTOR抑制显示出治疗急性髓性白血病 (AML) 的前景. 这种组合疗法在单细胞AML中特别有效,向特定的基因配置文件以诱导癌细胞死亡.

科学领域:

血液学血液学血液学
在瘤学瘤学.
分子生物学分子生物学

背景情况:

在急性髓性白血病 (AML) 中,mTOR信号传递至关重要,但单独使用mTOR抑制剂的临床疗效有限.
TP53通路在AML治疗反应中的作用需要进一步阐明.

研究的目的:

研究mTOR抑制和TP53激活在AML中的联合治疗效果.
在AML亚型中确定针对mTOR向治疗的预测生物标志物.

主要方法:

在AML细胞系和小鼠模型中利用了拉巴胺素 (mTOR抑制剂) 和DS-5272 (TP53激活剂).
在公共反洗钱数据库上使用联合非负矩阵分解 (JNMF) 和统计回归.
开发了一个11基因评分 (Rapa-11) 来预测拉巴胺素敏感性.

主要成果:

结合mTOR/TP53抑制通过miR-34a下调MYC和MCL1,诱导AML细胞的细胞亡和细胞循环停止.
单细胞AML具有特定的基因表达特征,通过Rapa-11得分识别,对拉帕素敏感.
与拉帕和DS-5272同时治疗可以治愈85%的MLL-AF9驱动的AML小鼠模型.

结论:

单细胞AML与野生型TP53和低Rapa-11分数是一种对mTOR抑制敏感的亚型.
mTOR/p53联合向治疗对于AML患者的一个子集来说是一个有前途的策略.
机器学习确定了针对性AML治疗的预测生物标志物.

关键词:

这就是TP53的特点.急性骨髓性白血病 (AML) 是一种急性骨髓性白血病.联合非负矩阵因数分解.在mTOROR中使用mTOR.机器学习是机器学习.

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