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

结直肠癌（CRC）仍然是美国最致命的癌症之一， 5-转移性疾病患者的年生存率为10%。超过120，000人被诊断患有CRC 每年约有5万人死亡即使采用目前的护理标准，CRC患者 高复发率和对治疗的抗药性是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性的来源。癌症相关 成纤维细胞（CAF）是肿瘤基质的主要细胞组分，并且在药物治疗中起重要作用。 通过促进作为CRC的标志的代谢改变而产生耐药性。最近的研究表明 CRC细胞和CAF之间的相互代谢重编程。然而，问题仍然存在， 这两种细胞群在治疗反应背景下的代谢依赖性。因此，在本发明中， 定量肿瘤和CAF细胞在其各自的细胞周期中的集体细胞动力学（即合作或竞争）， 代谢生态系统可以提供开发最佳癌症疗法所需的洞察力。 尽管有许多结直肠癌生长和进展的计算模型，但目前还没有 定量时空描述结肠癌细胞和基质细胞之间的相互作用，或 这两种细胞群的代谢依赖性。拟议的研究通过以下方式解决了这一限制： 开发一个基于实验的，多尺度的肿瘤间质代谢相互作用的计算模型， 结肠癌我们假设，利用肿瘤间质代谢依赖性将增强 抑制肿瘤生长的治疗策略。我们将通过使用系统生物学方法来检验这一假设 并追求三个目标，结合联合收割机计算和实验研究：（1）发展计算 CRC细胞和CAF中促进结肠癌增殖的细胞内代谢途径的模型;（2） 开发结肠癌细胞生长的空间多尺度模型，整合肿瘤-CAF的途径模型 代谢串扰;和（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型数学框架组成， 定量成像技术和生理学相关的临床前模型。我们组建了一个 一个充满活力的主要研究者团队成功完成了这个项目，整合了以下方面的专业知识： 计算系统生物学（由Finley领导）和模拟生物化学信号中的多细胞相互作用 环境（由Macklin领导），由尖端的高通量实验数据驱动， 条件（由Mummarter领导）。因此，这项工作将产生第一个多尺度模型， 解释了结直肠癌中肿瘤和基质细胞之间的分子相互作用。我们将 应用该模型来识别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，模型预测将通过实验验证。

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.