Personalized cancer treatment strategies incorporating irreversible and reversible drug resistance mechanisms.

IF 3.5 2区生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY

NPJ Systems Biology and Applications Pub Date : 2025-07-03 DOI:10.1038/s41540-025-00547-5

Wei He, Matthew D McCoy, Rebecca B Riggins, Robert A Beckman, Chen-Hsiang Yeang

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引用次数: 0

Abstract

Despite advances in targeted cancer therapy, the promise of precision medicine has been limited by resistance to these treatments. In this study, we propose a mathematical modelling framework incorporating cellular heterogeneity, genetic evolutionary dynamics, and non-genetic plasticity, accounting for both irreversible and reversible drug resistance. Previously we proposed Dynamic Precision Medicine (DPM), a personalized treatment strategy that designed individualized treatment sequences by simulations of irreversible genetic evolutionary dynamics in a heterogeneous tumor. Here we apply DPM to the joint model of reversible and irreversible drug resistance mechanisms, analyze the simulation results and compare the efficacy of various treatment strategies. The results indicate that this enhanced version of DPM significantly outperforms current personalized medicine treatment approaches. Our results provide insights into cancer treatment strategies for heterogeneous tumors with genetic evolutionary dynamics and non-genetic cellular plasticity, potentially leading to improvements in survival time for cancer patients.

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结合不可逆和可逆耐药机制的个性化癌症治疗策略。

尽管靶向癌症治疗取得了进展，但精准医疗的前景一直受到对这些治疗的耐药性的限制。在这项研究中，我们提出了一个结合细胞异质性、遗传进化动力学和非遗传可塑性的数学模型框架，考虑了不可逆和可逆的耐药性。在此之前，我们提出了动态精准医学（DPM），这是一种个性化治疗策略，通过模拟异质肿瘤中不可逆的遗传进化动力学来设计个性化治疗序列。本文将DPM应用于可逆和不可逆耐药机制联合模型，分析仿真结果，比较各种治疗策略的疗效。结果表明，这种增强版的DPM显着优于目前的个性化医疗治疗方法。我们的研究结果为具有遗传进化动力学和非遗传细胞可塑性的异质性肿瘤的癌症治疗策略提供了见解，可能导致癌症患者生存时间的改善。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

NPJ Systems Biology and Applications Mathematics-Applied Mathematics

CiteScore

5.80

自引率

0.00%

发文量

审稿时长

8 weeks

期刊介绍： npj Systems Biology and Applications is an online Open Access journal dedicated to publishing the premier research that takes a systems-oriented approach. The journal aims to provide a forum for the presentation of articles that help define this nascent field, as well as those that apply the advances to wider fields. We encourage studies that integrate, or aid the integration of, data, analyses and insight from molecules to organisms and broader systems. Important areas of interest include not only fundamental biological systems and drug discovery, but also applications to health, medical practice and implementation, big data, biotechnology, food science, human behaviour, broader biological systems and industrial applications of systems biology. We encourage all approaches, including network biology, application of control theory to biological systems, computational modelling and analysis, comprehensive and/or high-content measurements, theoretical, analytical and computational studies of system-level properties of biological systems and computational/software/data platforms enabling such studies.