量化内异质性和免疫架构由空间定量系统药理模型在体内生成
Mehdi Nikfar1, Haoyang Mi1, Chang Gong2
1Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA.
Cancers
|June 22, 2023
概括
了解瘤异质性是有效治疗癌症的关键. 这项研究表明,分隔的瘤微环境 (TME) 与抗PD-1治疗的更好的结果有关,为治疗开发提供了洞察力.
科学领域:
- 计算生物学和癌症研究.
- 量化系统药理学和基于代理物的建模.
背景情况:
- 癌症的空间异质性影响治疗反应.
- 由于数据的局限性,准确量化内异质性 (ITH) 是一个挑战.
研究的目的:
- 开发和应用一个混合模型,集成定量系统药理学 (QSP) 和基于代理的建模 (ABM).
- 量化评估ITH并对瘤微环境 (TME) 免疫架构进行分类.
- 为了将TME免疫架构与抗PD-1疗法的疗效相关联.
主要方法:
- 整合了一个整体患者的QSP模型与TME的空间ABM.
- 应用了四个空间指标 (混合得分,邻近频率,,G交叉AUC) 和一个非空间指标 (癌症与免疫细胞的比率).
- 根据这些指标,TME免疫架构被分类为"冷","分隔"或"混合".
主要成果:
- 混合模型模拟了ITH和TME免疫架构.
- 分类的TME类型与差异性的抗PD-1治疗反应有关.
- 对有效与无效治疗的指标的观察趋势与临床文献一致.
结论:
- 分隔的TME免疫架构与更有效的抗PD-1治疗结果有关.
- 量化指标可以对TME进行分类,并预测治疗反应.
- 这种建模方法为理解ITH和指导治疗策略提供了一个框架.
相关概念视频
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