基于代理的前列腺肿瘤微环境建模揭示了肿瘤生长的空间限制和免疫调节特性

IF 3.5 2区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Maisa N. G. van Genderen, Jeroen Kneppers, Anniek Zaalberg, Elise M. Bekers, Andries M. Bergman, Wilbert Zwart, Federica Eduati
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引用次数: 0

摘要

通过雄激素剥夺疗法(ADT)抑制雄激素受体(AR)信号传导,几乎可以减少所有患者的前列腺癌(PCa)生长,但反应可能是暂时的,在这种情况下会产生耐药性,最终导致致命的阉割耐药前列腺癌(CRPC)。肿瘤微环境(TME)在 PCa 的发展和恶化过程中发挥着重要作用。除肿瘤细胞外,TME 驻留的巨噬细胞和成纤维细胞也表达 AR,因此也会受到 ADT 的影响。然而,不同TME细胞类型在CRPC发展过程中的相互作用在很大程度上仍未得到探索。为了了解细胞-细胞相互作用的复杂随机性,我们根据体外细胞增殖数据创建了一个 PCa 特异性代理模型(PCABM)。PCa 细胞、成纤维细胞、"促炎 "的 M1 样和 "促瘤 "的 M2 样极化巨噬细胞都是根据一套简单的、经过验证的基本假设建立的代理模型。PCABM 使我们能够模拟 ADT 对各种前列腺 TME 细胞类型之间相互作用的影响。由此产生的体外生长模式模拟了人类 PCa。我们的 PCABM 能有效模拟 ADT 对激素的干扰,其中 PCABM 表明 CRPC 是在抗性细胞群中产生的,就像在多灶性 PCa 中观察到的那样。此外,成纤维细胞会争夺TME中的细胞空间,同时为肿瘤细胞增殖创造壁龛。最后,PCABM 预测 ADT 对巨噬细胞有免疫调节作用,可能会提高肿瘤的存活率。综上所述,这些结果表明 AR 在 TME 中影响肿瘤细胞行为和 CRPC 发展的细胞相互作用和随机互动中起着至关重要的作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Agent-based modeling of the prostate tumor microenvironment uncovers spatial tumor growth constraints and immunomodulatory properties

Agent-based modeling of the prostate tumor microenvironment uncovers spatial tumor growth constraints and immunomodulatory properties

Inhibiting androgen receptor (AR) signaling through androgen deprivation therapy (ADT) reduces prostate cancer (PCa) growth in virtually all patients, but response may be temporary, in which case resistance develops, ultimately leading to lethal castration-resistant prostate cancer (CRPC). The tumor microenvironment (TME) plays an important role in the development and progression of PCa. In addition to tumor cells, TME-resident macrophages and fibroblasts express AR and are therefore also affected by ADT. However, the interplay of different TME cell types in the development of CRPC remains largely unexplored. To understand the complex stochastic nature of cell-cell interactions, we created a PCa-specific agent-based model (PCABM) based on in vitro cell proliferation data. PCa cells, fibroblasts, “pro-inflammatory” M1-like and “pro-tumor” M2-like polarized macrophages are modeled as agents from a simple set of validated base assumptions. PCABM allows us to simulate the effect of ADT on the interplay between various prostate TME cell types. The resulting in vitro growth patterns mimic human PCa. Our PCABM can effectively model hormonal perturbations by ADT, in which PCABM suggests that CRPC arises in clusters of resistant cells, as is observed in multifocal PCa. In addition, fibroblasts compete for cellular space in the TME while simultaneously creating niches for tumor cells to proliferate in. Finally, PCABM predicts that ADT has immunomodulatory effects on macrophages that may enhance tumor survival. Taken together, these results suggest that AR plays a critical role in the cellular interplay and stochastic interactions in the TME that influence tumor cell behavior and CRPC development.

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来源期刊
NPJ Systems Biology and Applications
NPJ Systems Biology and Applications Mathematics-Applied Mathematics
CiteScore
5.80
自引率
0.00%
发文量
46
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.
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