Mathematical modeling of metabolism rewiring in cancer eco-evolution and metastasis tropism

总结 确定器官特异性转移的原因至关重要;如果没有这些知识， 并且转移性癌症的及时治疗可能仍然有限。本申请旨在开发新颖的 数学模型来理解重新连接的细胞代谢如何使癌细胞起源于 一个器官，如乳房，以殖民远端器官，如肺，脑，骨，其中有 不同的微环境。我们将研究亲代细胞及其转移衍生物的代谢重组 并询问原发性肿瘤中的代谢梯度如何产生和维持具有特异性的不同谱系， 器官特异性转移的代谢适应。我们的核心假设是：1）代谢适应 是转移性乳腺癌中种子（播散细胞）和土壤（远端部位）之间匹配的关键。 癌症，以及2）原发性肿瘤中的代谢微环境驱动代谢多样性 亚群这些假设是基于1）详细描述代谢的已发表数据 异质性和代谢适应可促进转移; 2）RNA初步数据和分析 表达，代谢组学和通量测量，揭示乳腺肿瘤中不同的代谢适应 3）初步数据显示，转移性谱系的反应， 与缺氧和营养梯度不同，表明代谢微环境在维持 同一异质性原发肿瘤内的不同亚群。数学建模对于 综合实验数据并推断不能直接测量代谢通量的变化。的 应用程序提出了一种研究策略，结合实验，临床和数学分析， 发现转移性癌细胞的新弱点。我们还将开发新的数学模型来研究 细胞系与其微环境之间的生态相互作用，并决定了 导致原发性肿瘤中代谢上不同的转移前亚群共存。

SUMMARY It is critically important to establish the causes of organ-specific metastasis; without this knowledge, prevention and timely treatment of metastatic cancer will likely remain limited. This application aims to develop novel mathematical models to understand how a rewired cellular metabolism enables cancer cells that originate in one organ such as the breast to colonize distal organs such as the lung, the brain, and the bone, which have distinct microenvironments. We will study metabolic rewiring in parental cells and their metastatic derivatives and ask how metabolic gradients in the primary tumor can generate and maintain diverse lineages with specific metabolic adaptations for organ-specific metastasis. Our central hypotheses are 1) that metabolic adaptations are key to the match between the seed (the disseminated cell) and the soil (the distal site) in metastatic breast cancer, and 2) that the metabolic microenvironment in a primary tumor drives metabolically diverse subpopulations. The hypotheses have been formulated based on 1) published data detailing metabolic heterogeneity and that metabolic adaptations can promote metastasis, 2) preliminary data and analysis of RNA expression, metabolomics, and flux measurements, revealing different metabolic adaptations in breast tumor cells that home to different tissues, and 3) preliminary data showing that metastatic lineages respond differently to hypoxia and nutrient gradients, indicating a role for the metabolic microenvironment in maintaining diverse subpopulations within the same heterogeneous primary tumor. Mathematical modeling is critical to integrate experimental data and infer changes in metabolic fluxes that cannot be directly measured. The application proposes a research strategy that combines experimental, clinical, and mathematical analysis to identify new vulnerabilities in metastatic cancer cells. We will also develop novel mathematical models to study the ecological interactions between cell lines and their microenvironment and determine the conditions that lead to coexistence of metabolically distinct pre-metastatic subpopulations in the primary tumor.