Mohammad Mirzaei Navid, Kevrekidis Panayotis G, Shahriyari Leili
Department of Epidemiology, Mailman School of Public Health, Columbia University, New York 10032, USA.
Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA 01003-4515, USA.
R Soc Open Sci. 2024 Dec 11;11(12):240718. doi: 10.1098/rsos.240718. eCollection 2024 Dec.
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.
乳腺癌是女性面临的一个具有挑战性的全球健康问题。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,研究复杂的乳腺肿瘤微环境(TME)动态变化。它纳入了内皮细胞(ECs)、氧气和血管内皮生长因子(VEGF),以研究血管生成、缺氧、VEGF和免疫细胞在癌症进展中的相互作用。我们引入了一种方法,利用来自MMTV-PyMT小鼠的单细胞RNA测序(scRNA-seq)数据来估算TME内的免疫细胞分数。我们通过使用细胞大小数据和现有文献中的实验室结果来估计细胞计数,从而对我们的分析进行量化。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生长和氧气运输中的作用,以及严重缺氧对癌症和细胞总数的影响。ECs的VEGF介导的产生率显示出重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抑制剂的疗效,并建议及时干预以获得更好的结果。