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机器学习框架用于结直肠癌的分化疗法

Saptarshi Sinha¹, Joshua Alcantara², Kevin Perry²

¹Department of Cellular and Molecular Medicine, School of Medicine, University of California, San Diego, La Jolla, CA 92093, USA.

Cell reports. Medicine

|October 21, 2025

概括

一个新的机器学习框架,CANDiT,确定PRKAB1作为消除结直肠癌干细胞 (CSCs) 的目标. 激活这一目标促进了差异化,降低了癌症患者的复发和死亡风险.

科学领域:

在瘤学瘤学.
计算生物学计算生物学
分子生物学分子生物学

背景情况:

癌症干细胞 (CSCs) 是一个治疗挑战,因为它们对常规治疗具有抗性.
重新激活细胞分化是消除CSC的一个有希望的策略.
结肠直肠癌 (CRC) 常常涉及攻击性亚型的CDX2等关键血统因子的丧失.

研究的目的:

开发一个机器学习框架 (CANDiT) 来识别治疗目标,以诱导CRC中的CSC差异化.
确定CRC中与CSC相关的特定分子漏洞.
评估针对已识别的弱点的治疗潜力,以消除CSC.

主要方法:

开发CANDiT,一种机器学习框架,分析转录数据以找到差异化漏洞.
确定PRKAB1,一个应力极性传感器,作为一个关键目标,特别是在CDX2-低的CRC细胞中.
在各种CRC模型 (细胞系,异种移植,患者衍生器官) 中测试PRKAB1激动剂.

主要成果:

该PRKAB1激动剂成功地重新激活了肠道血统程序,并拆除了茎状性通路 (Wnt/YAP).
在所有测试的CRC模型中观察到CDX2-低CSC的选择性消除.
一个强大的治疗指数与低CDX2的CSC状态有关.
一个50基因的预测特征表明,潜在的~50%降低了CRC复发和死亡风险.

结论:

通过诱导血统恢复,CANDiT提供了一个精确的框架来准CSC.
激活PRKAB1是消除CRC中CSC的一种可行的治疗策略.
识别的基因特征可以预测CRC的治疗反应和患者的结果.

关键词:

CCDC88A 这是一本书.在 CDX2 恢复中恢复CDX2.这些是SPSS.癌症干细胞是一种癌症干细胞.差异化疗法是一种差异化疗法.压力-极性路径的路径.

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