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基于深度学习的可扩展癌症器官芯片用于癌症精准医学
Yu-Chieh Yuan1, Beibei Xu2, Jenna McCormack1
1Xellar Biosystems, Boston, Massachusetts, USA.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|February 3, 2026
概括
一个新的器官芯片 (OC) 平台为功能精确瘤学提供了一个可扩展的,低成本的解决方案. 该系统允许使用患者衍生模型进行高通量药物敏感性测试,与临床结果保持密切一致.
科学领域:
- 生物技术是生物技术.
- 在瘤学瘤学.
- 微流体学 微流体学
背景情况:
- 功能精确瘤学旨在通过在患者衍生模型上测试疗法来个性化癌症治疗.
- 现有的模型,如患者衍生异种移植 (PDX) 和患者衍生器官 (PDO) 面临挑战,包括成本,时间,可扩展性和TME回顾.
研究的目的:
- 开发一个可扩展,低成本的器官芯片 (OC) 平台,用于功能精确瘤学.
- 克服当前药物敏感性测试患者衍生模型的局限性.
主要方法:
- 从热塑性塑料制造器官芯片 (OC) 平台,使用注塑成型.
- 采用专利的通道几何和表面处理,通过毛细血管结实现无障碍的水凝封闭.
- 集成深度学习用于无标签的表型分析和药物反应预测.
主要成果:
- 展示了一个可扩展,低成本的OC平台,支持各种矩阵和共同文化,具有强大的成像能力.
- 在细胞系和原始细胞上成功进行药物敏感性测试,结果与临床结果一致.
- 开发了一个深度学习模型,用于准确,纵向,无标签的表型分析.
结论:
- 开发的器官芯片 (OC) 系统解决了功能精确瘤学的关键技术障碍.
- 这个平台为高通量,与患者相关的药物测试提供了一个有希望的框架.
- 综合系统提高了灵敏度,并使得针对个性化癌症治疗的无标签分析成为可能.
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