Multiscale systems biology modeling to exploit tumor-stromal metabolic crosstalk in colorectal cancer

项目摘要：结直肠癌(CRC)仍然是美国最致命的癌症之一， 转移性疾病患者的5年生存率为10%。超过12万人被诊断为结直肠癌 每年，导致大约50,000人死亡。即使在目前的护理标准下，结直肠癌患者也有 高复发率和对治疗的抵抗是导致其高发病率和死亡率的关键因素。 肿瘤和基质细胞之间的相互作用是获得性耐药的一个来源。与癌症相关 成纤维细胞是肿瘤间质的主要细胞成分，在药物研究中发挥着重要作用。 耐药是导致新陈代谢改变的原因，而新陈代谢改变是结直肠癌的一个特征。最近的研究表明 结直肠癌细胞和CAF之间的相互代谢重编程。然而，关于以下问题仍然存在 在治疗反应的背景下，这两个细胞群体的代谢依赖性。因此， 量化肿瘤和CAF细胞在其体内的集体细胞动力学(即合作或竞争) 代谢生态系统可能提供开发最佳癌症疗法所需的洞察力。 尽管有许多关于结直肠癌生长和进展的计算模型，但目前还没有 结肠癌细胞与间质细胞相互作用的时空定量描述 这两个细胞群体的代谢依赖性。拟议的研究通过以下方式解决了这一限制 建立基于实验的肿瘤-间质代谢相互作用的多尺度计算模型 结肠癌。我们假设，利用肿瘤间质代谢依赖将增强 抑制肿瘤生长的治疗策略。我们将使用系统生物学的方法来检验这一假说。 追求计算和实验研究相结合的三个目标：(1)发展计算 促进结肠癌增殖的CRC细胞和CAF细胞内代谢途径的模型；(2) 结合肿瘤-CAF途径模型，建立结肠癌细胞生长的空间多尺度模型 代谢串扰；以及(3)识别和验证利用肿瘤和CAF代谢的治疗策略。 这项工作应用了一种系统生物学方法，该方法由跨尺度的新数学框架组成， 定量成像技术和生理相关的临床前模型。我们已经组建了一支 首席调查人员的动态团队成功完成此项目，整合了 计算系统生物学(由Finley领导)和生化信号中的多细胞相互作用建模 环境(由Macklin领导)，由现实中的尖端高通量实验数据驱动 条件(由穆门塔勒带头)。因此，这项工作将产生第一个明确的多尺度模型 在结直肠癌的背景下，解释了肿瘤和基质细胞之间的分子相互作用。我们会 应用该模型确定通过利用肿瘤基质细胞来抑制肿瘤生长的新策略 代谢相互作用，模型预测将得到实验验证。

Project Summary: Colorectal cancer (CRC) remains one of the deadliest cancers in the United States, with a 5-year survival rate of 10% for patients with metastatic disease. Over 120,000 people are diagnosed with CRC each year, leading to approximately 50,000 deaths. Even with the current standard of care, CRC patients have a high rate of relapse, and resistance to therapy is a key contributor to their high morbidity and mortality. Interactions between tumor and stromal cells are a source of acquired drug resistance. Cancer-associated fibroblasts (CAFs) are a dominant cellular component of the tumor stroma and play a significant role in drug resistance by contributing to the altered metabolism that is a hallmark of CRC. Recent studies suggest reciprocal metabolic reprogramming among CRC cells and CAFs. However, questions still remain regarding the metabolic dependencies of these two cell populations in the context of treatment response. Thus, quantifying the collective cell dynamics (i.e. cooperation or competition) of tumor and CAF cells in their metabolic ecosystem may provide insight needed to develop optimal cancer therapies. Despite many computational models of colorectal cancer growth and progression, there is currently no quantitative spatiotemporal description of the interactions between colon cancer cells and stromal cells, or the metabolic dependencies of these two cell populations. The proposed research addresses this limitation by developing an experiment-based, multiscale computational model of tumor-stromal metabolic interactions in colon cancer. We hypothesize that exploiting tumor-stromal metabolic dependencies will enhance the effects of therapeutic strategies to inhibit tumor growth. We will test this hypothesis by using a systems biology approach and pursuing three aims that combine computational and experimental studies: (1) Develop computational models of intracellular metabolic pathways in CRC cells and CAFs that promote colon cancer proliferation; (2) Develop a spatial multiscale model of colon cancer cell growth, integrating the pathway models of tumor-CAF metabolic crosstalk; and (3) Identify and validate treatment strategies that exploit tumor and CAF metabolism. This work applies a systems biology approach comprised of novel mathematical frameworks across scales, quantitative imaging techniques, and physiologically-relevant preclinical models. We have assembled a dynamic team of Principal Investigators to successfully complete this project, integrating expertise in computational systems biology (lead by Finley) and modeling multicellular interactions in biochemical signaling environments (lead by Macklin), driven by cutting-edge high-throughput experimental data in realistic conditions (lead by Mumenthaler). As a result, this work will generate the first multiscale model that explicitly accounts for molecular interactions between tumor and stromal cells in the context of colorectal cancer. We will apply the model to identify novel strategies that inhibit tumor growth by exploiting the tumor-stromal cell metabolic interactions, and the model predictions will be validated experimentally.