A resource-based mechanistic framework for castration-resistant prostate cancer (CRPC)

IF 2 4区 数学 Q2 BIOLOGY Journal of Theoretical Biology Pub Date : 2024-06-21 Epub Date: 2024-04-02 DOI:10.1016/j.jtbi.2024.111806
B. Vibishan , Harshavardhan B.V. , Sutirth Dey
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Abstract

Cancer therapy often leads to the selective elimination of drug-sensitive cells from the tumour. This can favour the growth of cells resistant to the therapeutic agent, ultimately causing a tumour relapse. Castration-resistant prostate cancer (CRPC) is a well-characterised instance of this phenomenon. In CRPC, after systemic androgen deprivation therapy (ADT), a subset of drug-resistant cancer cells autonomously produce testosterone, thus enabling tumour regrowth. A previous theoretical study has shown that such a tumour relapse can be delayed by inhibiting the growth of drug-resistant cells using biotic competition from drug-sensitive cells. In this context, the centrality of resource dynamics to intra-tumour competition in the CRPC system indicates clear scope for the construction of theoretical models that can explicitly incorporate the underlying mechanisms of tumour ecology. In the current study, we use a modified logistic framework to model cell–cell interactions in terms of the production and consumption of resources. Our results show that steady state composition of CRPC can be understood as a composite function of the availability and utilisation efficiency of two resources-oxygen and testosterone. In particular, we show that the effect of changing resource availability or use efficiency is conditioned by their general abundance regimes. Testosterone typically functions in trace amounts and thus affects steady state behaviour of the CRPC system differently from oxygen, which is usually available at higher levels. Our data thus indicate that explicit consideration of resource dynamics can produce novel and useful mechanistic understanding of CRPC. Furthermore, such a modelling approach also incorporates variables into the system’s description that can be directly measured in a clinical context. This is therefore a promising avenue of research in cancer ecology that could lead to therapeutic approaches that are more clearly rooted in the biology of CRPC.

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基于资源的耐受性前列腺癌(CRPC)机理框架
癌症治疗通常会选择性地清除肿瘤中对药物敏感的细胞。这可能有利于对治疗药物产生抗药性的细胞生长,最终导致肿瘤复发。阉割耐药前列腺癌(CRPC)就是这种现象的一个典型例子。在 CRPC 中,经过全身性雄激素剥夺疗法(ADT)后,一部分耐药癌细胞会自主产生睾酮,从而使肿瘤重新生长。之前的一项理论研究表明,利用药物敏感细胞的生物竞争抑制耐药细胞的生长,可以延缓肿瘤复发。在这种情况下,CRPC 系统中资源动态对肿瘤内竞争的核心作用为构建明确包含肿瘤生态学基本机制的理论模型提供了明确的空间。在目前的研究中,我们使用了一个改进的逻辑框架,以资源的生产和消耗来模拟细胞与细胞之间的相互作用。我们的研究结果表明,CRPC 的稳态组成可以理解为氧气和睾酮这两种资源的可用性和利用效率的复合函数。特别是,我们表明,改变资源可用性或利用效率的效果受这两种资源一般丰度机制的制约。睾酮通常以痕量形式发挥作用,因此对 CRPC 系统稳态行为的影响不同于氧气,后者的可用水平通常更高。因此,我们的数据表明,对资源动态的明确考虑可以对 CRPC 产生新颖而有用的机理认识。此外,这种建模方法还将可在临床环境中直接测量的变量纳入系统描述中。因此,这是癌症生态学研究中一条前景广阔的途径,它能带来更明确植根于 CRPC 生物学的治疗方法。
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来源期刊
CiteScore
4.20
自引率
5.00%
发文量
218
审稿时长
51 days
期刊介绍: The Journal of Theoretical Biology is the leading forum for theoretical perspectives that give insight into biological processes. It covers a very wide range of topics and is of interest to biologists in many areas of research, including: • Brain and Neuroscience • Cancer Growth and Treatment • Cell Biology • Developmental Biology • Ecology • Evolution • Immunology, • Infectious and non-infectious Diseases, • Mathematical, Computational, Biophysical and Statistical Modeling • Microbiology, Molecular Biology, and Biochemistry • Networks and Complex Systems • Physiology • Pharmacodynamics • Animal Behavior and Game Theory Acceptable papers are those that bear significant importance on the biology per se being presented, and not on the mathematical analysis. Papers that include some data or experimental material bearing on theory will be considered, including those that contain comparative study, statistical data analysis, mathematical proof, computer simulations, experiments, field observations, or even philosophical arguments, which are all methods to support or reject theoretical ideas. However, there should be a concerted effort to make papers intelligible to biologists in the chosen field.
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