Oxygen, angiogenesis, cancer and immune interplay in breast tumour microenvironment: a computational investigation.

IF 2.9 3区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Royal Society Open Science Pub Date : 2024-12-11 eCollection Date: 2024-12-01 DOI:10.1098/rsos.240718
Navid Mohammad Mirzaei, Panayotis G Kevrekidis, Leili Shahriyari
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引用次数: 0

Abstract

Breast cancer is a challenging global health problem among women. This study investigates the intricate breast tumour microenvironment (TME) dynamics utilizing data from mammary-specific polyomavirus middle T antigen overexpression mouse models (MMTV-PyMT). It incorporates endothelial cells (ECs), oxygen and vascular endothelial growth factors (VEGF) to examine the interplay of angiogenesis, hypoxia, VEGF and immune cells in cancer progression. We introduce an approach to impute immune cell fractions within the TME using single-cell RNA-sequencing (scRNA-seq) data from MMTV-PyMT mice. We quantify our analysis by estimating cell counts using cell size data and laboratory findings from existing literature. We perform parameter estimation via a Hybrid Genetic Algorithm (HGA). Our simulations reveal various TME behaviours, emphasizing the critical role of adipocytes, angiogenesis, hypoxia and oxygen transport in driving immune responses and cancer progression. Global sensitivity analyses highlight potential therapeutic intervention points, such as VEGFs' role in EC growth and oxygen transportation and severe hypoxia's effect on cancer and the total number of cells. The VEGF-mediated production rate of ECs shows an essential time-dependent impact, highlighting the importance of early intervention in slowing cancer progression. These findings align with clinical observations demonstrating the VEGF inhibitors' efficacy and suggest a timely intervention for better outcomes.

氧、血管生成、肿瘤和免疫在乳腺肿瘤微环境中的相互作用:一个计算研究。
乳腺癌是全球妇女面临的一个具有挑战性的健康问题。本研究利用乳腺特异性多瘤病毒中T抗原过表达小鼠模型(MMTV-PyMT)的数据研究了复杂的乳腺肿瘤微环境(TME)动力学。它结合内皮细胞(ECs)、氧和血管内皮生长因子(VEGF)来研究血管生成、缺氧、VEGF和免疫细胞在癌症进展中的相互作用。我们介绍了一种利用MMTV-PyMT小鼠的单细胞rna测序(scRNA-seq)数据在TME内推断免疫细胞组分的方法。我们通过使用现有文献中的细胞大小数据和实验室结果估计细胞计数来量化我们的分析。我们通过混合遗传算法(HGA)进行参数估计。我们的模拟揭示了各种TME行为,强调了脂肪细胞、血管生成、缺氧和氧气运输在驱动免疫反应和癌症进展中的关键作用。全局敏感性分析突出了潜在的治疗干预点,如vegf在EC生长和氧运输中的作用,以及严重缺氧对癌症和细胞总数的影响。vegf介导的内皮细胞生成速率显示出必要的时间依赖性影响,突出了早期干预在减缓癌症进展中的重要性。这些发现与临床观察结果一致,证明了VEGF抑制剂的有效性,并建议及时干预以获得更好的结果。
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
Royal Society Open Science
Royal Society Open Science Multidisciplinary-Multidisciplinary
CiteScore
6.00
自引率
0.00%
发文量
508
审稿时长
14 weeks
期刊介绍: Royal Society Open Science is a new open journal publishing high-quality original research across the entire range of science on the basis of objective peer-review. The journal covers the entire range of science and mathematics and will allow the Society to publish all the high-quality work it receives without the usual restrictions on scope, length or impact.
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