综合转录和功能建模揭示了AKT和mTOR在结直肠癌中的协同作用
Marcin Duleba1, Eliza Zimoląg2, Joanna Szuszkiewicz2
1Ryvu Therapeutics, Kraków, Poland. marcin.duleba@ryvu.com.
Scientific reports
|July 31, 2025
概括
开发患者衍生的结直肠癌模型和使用机器学习识别了新型药物组合. 对于个性化结直肠癌治疗来说,mTOR-AKT抑制是有前途的.
科学领域:
- 在瘤学瘤学.
- 药物发现 药物发现 药物发现
- 基因组学就是基因组学.
背景情况:
- 结肠直肠癌 (CRC) 治疗受到遗传多样性和耐药性的阻碍.
- 标准细胞系可能不能完全代表原发性瘤的复杂性和异质性.
研究的目的:
- 开发一个转化药物发现平台,使用来自患者的CRC培养物.
- 通过高通量查和机器学习来确定结直肠癌的新型治疗策略.
主要方法:
- 确立的患者衍生原发性结直肠癌培养体反映瘤异质性.
- 对4255种化合物进行了高通量选 (HTS).
- 将机器学习集成到HTS管道中,以提高效率和准确性.
- 评估的药物组合,包括Everolimus (mTOR抑制剂) 和uprosertib (AKT抑制剂).
主要成果:
- 确定了33种具有针对结直肠癌细胞的选择性疗效的化合物.
- 在临床上相关的度下发现了Everolimus和Uprosertib之间的协同作用.
- 证明了患者衍生模型相对于传统细胞系的翻译优势.
- 机器学习集成提高了HTS的可扩展性,成本效益和预测能力.
结论:
- 患者衍生模型与机器学习相结合,加速了个性化结直肠癌治疗的开发.
- 抑制mTOR-AKT是一种有希望的针对性策略,用于结直肠癌的治疗.
- 先进的模型对于发现可能被传统方法遗漏的协同药物相互作用至关重要.
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