血蛋白质组分析揭示了双阻断剂治疗后胆固醇标记物的动态
Jiacheng Lyu1, Lin Bai1, Yumiao Li2
1Center for Cell and Gene Therapy, Fudan University Clinical Research Center for Cell-based Immunotherapy, State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Human Phenome Institute, Shanghai Pudong Hospital, Fudan University, Shanghai, 200433, China.
双阻塞疗法 (DBT) 显示出改善的抗瘤效果. 这项研究确定了胆固醇代谢变化和APOC3作为生物标志物,用于使用血蛋白质组分析预测DBT反应.
科学领域:
- 免疫学 免疫学 免疫学
- 代谢学 代谢学 代谢学
- 在瘤学瘤学.
背景情况:
- 结合抗PD1和抗CTLA4的双阻塞疗法 (DBT) 提供了比单一疗法更好的抗瘤益处.
- 监测DBT反应的有效生物标志物仍然有限,阻碍了个性化治疗策略.
研究的目的:
- 在接受抗PD1和抗CTLA4DBT的患者中研究纵向血蛋白质组概况.
- 确定可预测DBT反应的生物标志物.
主要方法:
- 从22名接受DBT的患者113个样本的长度血蛋白质组分析.
- 蛋白质组数据与放射学发现的整合.
- 开发和验证用于DBT响应预测的机器学习模型.
主要成果:
- 免疫反应和胆固醇代谢在第一个DBT周期后得到了上调.
- 在非进展性疾病 (DNP) 组中观察到激活胆固醇代谢,特别是高密度脂蛋白重塑.
- 临床指标 (前专辑蛋白,FT3,T3) 与胆固醇代谢积极相关.
- APOC3被确定为DBT反应的候选生物标志物.
- 一个机器学习模型在预测独立队列的DBT响应时实现了0.96平衡的准确性.
结论:
- 血蛋白质组分析揭示了DBT期间胆固醇代谢的变化.
- 包括APOC3在内的生物标志物的小组可以有效地评估DBT反应.
- 开发的机器学习模型显示了DBT结果的高预测性能.
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