Department of Mathematics, College of Science, Swansea University, Swansea SA2 8PP, United Kingdom; Computational Foundry, Swansea University, Swansea SA2 8PP, United Kingdom.
Zienkiewicz Centre for Computational Engineering, Swansea University, Swansea SA1 8EN, United Kingdom.
J Theor Biol. 2018 Oct 7;454:253-267. doi: 10.1016/j.jtbi.2018.06.014. Epub 2018 Jun 15.
Tumour recurrence post chemotherapy is an established clinical problem and many cancer types are often observed to be increasingly drug resistant subsequent to chemotherapy treatments. Drug resistance in cancer is a multipart phenomenon which can be derived from several origins and in many cases it has been observed that cancer cells have the ability to possess, acquire and communicate drug resistant traits. Here, an in silico framework is developed in order to study drug resistance and drug response in cancer cell populations exhibiting various drug resistant features. The framework is based on an on-lattice hybrid multiscale mathematical model and is equipped to simulate multiple mechanisms on different scales that contribute towards chemotherapeutic drug resistance in cancer. This study demonstrates how drug resistant tumour features may depend on the interplay amongst intracellular, extracelluar and intercellular factors. On a cellular level, drug resistant cell phenotypes are here derived from inheritance or mutations that are spontaneous, drug-induced or communicated via exosomes. Furthermore intratumoural heterogeneity and spatio-temporal drug dynamics heavily influences drug delivery and the development of drug resistant cancer cell subpopulations. Chemotherapy treatment strategies are here optimised for various in silico tumour scenarios and treatment objectives. We demonstrate that optimal chemotherapy treatment strategies drastically depend on which drug resistant mechanisms are activated, and that furthermore suboptimal chemotherapy administration may promote drug resistance.
化疗后肿瘤复发是一个既定的临床问题，许多癌症类型在化疗后往往表现出越来越强的耐药性。癌症的耐药性是一种多方面的现象，它可以源自多个来源，在许多情况下，已经观察到癌细胞具有拥有、获得和传播耐药性特征的能力。在这里，开发了一种计算框架，以便研究表现出各种耐药特征的癌细胞群体中的耐药性和药物反应。该框架基于网格混合多尺度数学模型，能够模拟对癌症的化疗耐药性有贡献的不同尺度上的多种机制。本研究表明，耐药性肿瘤特征可能取决于细胞内、细胞外和细胞间因素之间的相互作用。在细胞水平上，耐药细胞表型源自自发的、药物诱导的或通过外泌体传递的遗传或突变。此外，肿瘤内异质性和时空药物动力学严重影响药物输送和耐药性癌细胞亚群的发展。在这里，针对各种虚拟肿瘤情况和治疗目标优化了化疗治疗策略。我们证明，最佳化疗治疗策略在很大程度上取决于哪些耐药机制被激活，而且，次优的化疗给药可能会促进耐药性。
