Pattern Formation as a Resilience Mechanism in Cancer Immunotherapy.

IF 2 4区 数学 Q2 BIOLOGY
Molly Brennan, Andrew L Krause, Edgardo Villar-Sepúlveda, Christopher B Prior
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引用次数: 0

Abstract

Mathematical and computational modelling in oncology has played an increasingly important role in not only understanding the impact of various approaches to treatment on tumour growth, but in optimizing dosing regimens and aiding the development of treatment strategies. However, as with all modelling, only an approximation is made in the description of the biological and physical system. Here we show that tissue-scale spatial structure can have a profound impact on the resilience of tumours to immunotherapy using a classical model incorporating IL-2 compounds and effector cells as treatment parameters. Using linear stability analysis, numerical continuation, and direct simulations, we show that diffusing cancer cell populations can undergo pattern-forming (Turing) instabilities, leading to spatially-structured states that persist far into treatment regimes where the corresponding spatially homogeneous systems would uniformly predict a cancer-free state. These spatially-patterned states persist in a wide range of parameters, as well as under time-dependent treatment regimes. Incorporating treatment via domain boundaries can increase this resistance to treatment in the interior of the domain, further highlighting the importance of spatial modelling when designing treatment protocols informed by mathematical models. Counter-intuitively, this mechanism shows that increased effector cell mobility can increase the resilience of tumours to treatment. We conclude by discussing practical and theoretical considerations for understanding this kind of spatial resilience in other models of cancer treatment, in particular those incorporating more realistic spatial transport. This paper belongs to the special collection: Problems, Progress and Perspectives in Mathematical and Computational Biology.

模式形成作为肿瘤免疫治疗的弹性机制。
肿瘤学中的数学和计算模型不仅在理解各种治疗方法对肿瘤生长的影响方面发挥着越来越重要的作用,而且在优化给药方案和帮助制定治疗策略方面也发挥着越来越重要的作用。然而,与所有的建模一样,在描述生物和物理系统时只做了一个近似。在这里,我们展示了组织尺度的空间结构可以对肿瘤免疫治疗的弹性产生深远的影响,使用经典模型将IL-2化合物和效应细胞作为治疗参数。通过线性稳定性分析、数值延拓和直接模拟,我们发现扩散的癌细胞群可以经历模式形成(图灵)不稳定性,导致空间结构状态持续到相应的空间均匀系统将统一预测无癌状态的治疗方案。这些空间模式状态在广泛的参数范围内持续存在,以及在时间依赖的治疗制度下。通过域边界进行治疗可以增加域内部对治疗的抵抗力,进一步强调了在设计由数学模型提供信息的治疗方案时空间建模的重要性。与直觉相反,这一机制表明,增加效应细胞的流动性可以增加肿瘤对治疗的恢复能力。最后,我们讨论了在其他癌症治疗模型中理解这种空间弹性的实践和理论考虑,特别是那些包含更现实的空间运输的模型。本文属于《数学与计算生物学的问题、进展与展望》特刊。
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
CiteScore
3.90
自引率
8.60%
发文量
123
审稿时长
7.5 months
期刊介绍: The Bulletin of Mathematical Biology, the official journal of the Society for Mathematical Biology, disseminates original research findings and other information relevant to the interface of biology and the mathematical sciences. Contributions should have relevance to both fields. In order to accommodate the broad scope of new developments, the journal accepts a variety of contributions, including: Original research articles focused on new biological insights gained with the help of tools from the mathematical sciences or new mathematical tools and methods with demonstrated applicability to biological investigations Research in mathematical biology education Reviews Commentaries Perspectives, and contributions that discuss issues important to the profession All contributions are peer-reviewed.
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