通过对它们的空间蛋白质组形状的反事实学习来识别促进T细胞透到瘤中的干扰
Zitong Jerry Wang1, Abdullah S Farooq2, Yu-Jen Chen2
1Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA. jerry@westlake.edu.cn.
Nature biomedical engineering
|March 5, 2025
概括
一个新的深度学习模型预测了最小的瘤变化,以增强T细胞透,这是癌症免疫疗法的关键步骤. 这种方法确定了特定的分子组合,以促进黑色素瘤和结直肠癌的抗癌免疫反应.
科学领域:
- 计算生物学是一种计算生物学.
- 免疫学 免疫学 免疫学
- 在瘤学瘤学.
背景情况:
- 瘤微环境中的T细胞透对于阻止癌症进展至关重要.
- 目前的策略在调节瘤微环境以优化T细胞活动时往往缺乏精度.
研究的目的:
- 开发一种深度学习模型,用于预测增强T细胞透的最小瘤干扰.
- 确定促进T细胞透在不同类型癌症中的特定分子组合.
主要方法:
- 利用深度学习模型整合反事实优化和自我监督学习来预测T细胞透.
- 将模型应用于368个转移性黑色素瘤和结直肠癌样本的空间蛋白质组概况,使用40倍位成像质细胞计.
- 通过体外实验验验证了预测的干扰.
主要成果:
- 发现了依赖于队列的组合性扰动,可显著提高T细胞透率.
- 针对黑色素瘤 (CXCL9,CXCL10,CCL22,CCL18) 和结直肠癌 (CXCR4,PD-1,PD-L1,CYR61) 确定了特定的分子组合.
- 证实了这些干扰在支持跨患者队伍的T细胞透方面的有效性.
结论:
- 来自空间奥米克数据的基于反事实的预测可以指导新型癌症治疗方法的设计.
- 这种深度学习方法提供了一个有前途的策略,通过优化T细胞透来增强免疫疗法.
- 鉴定到的干扰代表了开发更有效的癌症治疗方法的潜在目标.
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