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

癌症生态进化和转移倾向中代谢重连的数学模型

• 批准号: 10582078
• 负责人: Joao Xavier
• 金额: $ 57.57万
• 依托单位: SLOAN-KETTERING INST CAN RESEARCH
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2027-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10582078
• 关键词: 3-Dimensional; Affect; Automobile Driving; Biological Assay; Biopsy; Brain; Breast; Breast Cancer Cell; Breast Cancer Model; Cancer Etiology; Cancer Science; Cell Line; Cell Lineage; Cells; Cessation of life; Clinical; Coculture Techniques; Consumption; Data; Differential Equation; Diffusion; Disseminated Malignant Neoplasm; Distal; Drops; Equation; Evolution; Game Theory; Genetic Transcription; Glucose; Glutamine; Heterogeneity; Home; Homing; Hypoxia; Image Analysis; In Vitro; Individual; Knowledge; Label; Link; Lung; Malignant Neoplasms; Measurement; Measures; Metabolic; Metabolism; Metastatic breast cancer; Metastatic malignant neoplasm to brain; Microscopy; Modeling; Neoplasm Metastasis; Nutrient; Organ; Oxygen; Pancreas; Pathway interactions; Patients; Population; Population Heterogeneity; Prevention; Primary Neoplasm; Production; Publishing; Pyruvate; RNA analysis; Reaction; Research; Retinal blind spot; Role; Sampling; Series; Site; Soil; Spatial Distribution; Structure; System; Testing; Time; Tissues; Transcriptional Regulation; Tropism; Validation; Waste Products; bone; cancer cell; cell growth; cell motility; experimental study; in vivo; malignant breast neoplasm; mathematical analysis; mathematical model; metabolic abnormality assessment; metabolome; metabolomics; mimetics; mouse model; novel; particle; predictive modeling; prevent; simulation; solute; three-dimensional modeling; transcriptional reprogramming; transcriptomics; tumor; tumor heterogeneity; tumor metabolism

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.

概括 确定器官特异性转移的原因至关重要；如果没有这些知识，预防 转移性癌症的及时治疗可能仍然有限。该应用程序旨在开发新颖的 数学模型来了解重新连接的细胞代谢如何使起源于 一种器官，如乳房，定植于远端器官，如肺、大脑和骨骼，这些器官具有 独特的微环境。我们将研究亲本细胞及其转移衍生物的代谢重连 并询问原发肿瘤中的代谢梯度如何产生和维持具有特定特征的不同谱系 器官特异性转移的代谢适应。我们的中心假设是 1）代谢适应 是转移性乳腺中种子（播散细胞）和土壤（远端部位）匹配的关键 癌症，2) 原发肿瘤中的代谢微环境驱动代谢多样化 亚人群。这些假设是根据 1）已发表的详细代谢数据制定的 异质性以及代谢适应可以促进转移，2）RNA的初步数据和分析 表达、代谢组学和通量测量，揭示乳腺肿瘤的不同代谢适应 归巢到不同组织的细胞，以及 3) 初步数据显示转移谱系有反应 与缺氧和营养梯度不同，表明代谢微环境在维持 同一异质原发肿瘤内的不同亚群。数学建模对于 整合实验数据并推断无法直接测量的代谢通量的变化。这 应用程序提出了一种结合实验、临床和数学分析的研究策略 识别转移癌细胞的新弱点。我们还将开发新颖的数学模型来研究 细胞系与其微环境之间的生态相互作用，并确定细胞系的条件 导致原发肿瘤中代谢不同的转移前亚群共存。