针对癌症治疗的优化患者特异性免疫检查点抑制剂疗法,基于瘤免疫微环境建模
Yao Yao1, Youhua Frank Chen2, Qingpeng Zhang3,4
1School of Data Science, City University of Hong Kong, Kowloon, Hong Kong SAR 00001, China.
Briefings in bioinformatics
|October 25, 2024
概括
这项研究开发了一种数学模型和深度强化学习 (DRL),以个性化癌症免疫治疗. DRL剂优化免疫检查点抑制剂 (ICI) 和化疗组合,以改善患者的反应.
科学领域:
- 计算生物学 计算生物学
- 癌症研究 癌症研究
- 免疫治疗是一种免疫疗法.
背景情况:
- 提高患者对免疫检查点抑制剂 (ICI) 的反应在癌症免疫治疗中至关重要.
- 瘤免疫微环境 (TIME) 的复杂性对优化治疗策略提出了挑战.
研究的目的:
- 开发一个数据驱动的数学模型的时间.
- 利用深度强化学习 (DRL) 来优化患者特定的ICI与化疗 (ICC) 结合.
主要方法:
- 使用基因组和转录基因组数据创建了一个基于普通微分方程 (ODEs) 的TIME动态进化模型.
- 一名DRL代理人接受了培训,以确定个性化的最佳ICC治疗计划.
- 用现实数据进行了数值实验,以验证模型和DRL方法.
主要成果:
- 时间模型准确地预测了ICI治疗反应.
- 与固定时间表相比,DRL衍生的个性化ICC疗法显示出更好的结果.
- 根据瘤类型的治疗建议有所不同:"极度冷瘤"的高剂量化疗,以及"冷瘤"和"热瘤"的定制化疗-ICI组合.
结论:
- 该研究强调了整合真实世界的临床数据和DRL的潜力,以实现个性化的ICC优化.
- 基于ODE的TIME模型为理解ICI疗效提供了一个框架.
- DRL代理可以指导个性化的ICC调度,以改善癌症治疗.
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