瘤细胞结构的深度学习模型阐明了对CDK4/6抑制剂的反应和耐药性
Sungjoon Park1, Erica Silva2, Akshat Singhal3
1Department of Medicine, University of California, San Diego, La Jolla, CA, USA.
Nature cancer
|March 5, 2024
概括
深度学习模型预测了对乳腺癌中循环素依赖性激酶4和6抑制剂 (CDK4/6is) 的反应. 识别关键蛋白质组合揭示了CDK4/6抑制剂耐药性的机制.
科学领域:
- 在瘤学瘤学.
- 分子生物学分子生物学
- 计算生物学 计算生物学
背景情况:
- 循环素依赖性激酶4和6抑制剂 (CDK4/6is) 在乳腺癌治疗中至关重要.
- 患者的有限反应和对CDK4/6的获得性耐药性需要了解潜在的机制.
研究的目的:
- 开发一个可解释的深度学习模型,预测对CDK4/6抑制剂palbociclib的反应.
- 确定与CDK4/6抑制剂敏感性和耐药性相关的关键多蛋白组合.
主要方法:
- 使用癌症多蛋白组合的参考地图构建了一个深度学习模型.
- 整合了90个基因的基因改变,以分层细胞系.
- 在患者衍生的异种移植中验证了模型预测,并使用CRISPR-Cas9用于功能遗传破坏.
主要成果:
- 确定了八个核心多蛋白组合,可以预测palbociclib敏感性与耐药性.
- 模型预测与临床结果相关,表现优于单基因生物标志物.
- 经过验证的组件包括细胞周期控制,生长因子信号传递和组织素修饰 (KAT6A,TBL1XR1,RUNX1) 促进S相进入.
结论:
- 一个可解释的深度学习模型有效地预测了乳腺癌中CDK4/6抑制剂反应和耐药性.
- 多蛋白组合,而不是单个基因,对于分层治疗反应至关重要.
- 了解这些组合可以了解治疗耐药性和潜在的治疗策略.
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