通过从基因组衍生分子途径学习,个性化预测非瘤药物的抗癌潜力
Xiaobao Dong1, Huanhuan Liu2, Ting Tong3,4
1Department of Genetics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Precision Medicine Research Center, The Second Hospital of Tianjin Medical University; Tianjin Medical University, Tianjin, China.
NPJ precision oncology
|February 5, 2025
概括
机器学习模型CHANCE可以预测使用基因突变的个体患者的非瘤药物的抗癌活性. 这种方法可以为超过30%的癌症患者确定潜在的治疗方法,进步精确瘤学.
科学领域:
- 在瘤学瘤学.
- 基因组学就是基因组学.
- 药理学 药理学是指药理学的学科.
- 生物信息学是一种生物信息学.
背景情况:
- 癌症基因组学有助于理解,但药物开发是昂贵的.
- 重用已批准的非瘤药物提供了一个具有成本效益的治疗策略.
- 个性化医学需要根据个体患者的突变预测药物的疗效.
研究的目的:
- 开发一个监督的机器学习模型 (CHANCE) 来预测非瘤药物的抗癌活性.
- 整合个性化编码和非编码突变与药物信息进行准确的预测.
- 为癌症患者确定潜在的非瘤学药物治疗方法.
主要方法:
- 开发了一个受监督的机器学习模型CHANCE.
- 利用蛋白质-蛋白质相互作用网络来协调突变数据.
- 综合多层次突变注释和药理信息.
- 将模型应用于5000个癌症样本,并进行实验验证.
主要成果:
- 机会超越了以前的模型,并提供了可解释的预测.
- 在分析的癌症样本中,超过30%的样本对非瘤学药物显示出潜在的反应.
- 确定了SMAD7突变与阿司匹林反应之间的联系.
- 实验验证证证实了7个患者衍生瘤细胞系中的5个中药物的有效性.
结论:
- 在精密瘤学中,CHANCE是识别非瘤学药物的宝贵工具.
- 该模型能够根据患者特定突变进行个性化治疗预测.
- 使用CHANCE重定位药物可以显著扩大癌症患者的治疗选择.
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