一个基于记忆的强化学习模型,用于预测前列腺癌治疗耐药性的表型适应
Zahra S Ghoreyshi1,2,3, Shibjyoti Debnath4,5, Pelumi D Olawuni4,5
1Department of Biomedical Engineering, Texas A&M University, College Station, 77843, TX, USA.
bioRxiv : the preprint server for biology
|November 24, 2025
概括
这项研究引入了一种新的适应性癌症模型,使用强化学习来预测治疗耐药性和患者反应. 这种基于记忆的模型准确地预测前列腺癌的动态,并确定患者的治疗结果.
科学领域:
- 在瘤学瘤学.
- 计算生物学 计算生物学
- 系统生物学 系统生物学
背景情况:
- 治疗耐药性是癌症进展和死亡的主要原因.
- 癌症疗法可以触发细胞中的适应性生存反应.
- 预测治疗结果需要考虑治疗史的模型.
研究的目的:
- 开发一种整合强化学习的随机模型,用于预测对重复癌症治疗的反应.
- 为了捕捉历史依赖的细胞过渡和生长动态.
- 在前列腺癌治疗场景中应用和验证该模型.
主要方法:
- 使用强化学习开发了一种基于记忆的自适应模型.
- 综合环境史:依赖于细胞的转变和生长动态.
- 将模型应用于接受恩扎胺治疗的LNCaP细胞和患者的PSA数据.
主要成果:
- 适应模型准确地捕获了对药物敏感和耐药LNCaP细胞的实验观察动态.
- 在预测癌症细胞群体波动方面表现优于缺乏历史依赖的传统模型.
- 通过p38/MAPK抑制和患者PSA动态,成功预测了耐药细胞的控制.
结论:
- 开发的自适应模型为预测癌症治疗结果提供了一个强大的框架.
- 它提供了对前列腺癌进展和患者反应的定量见解.
- 这种方法可以指导开发优化,针对患者的适应性治疗策略.
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