Collaborative Research: Engineering Approaches to Cancer Metabolism to Interpret and Develop Improved Treatment Modalities

知识价值：自Warburg的工作以来，癌细胞表现出细胞代谢的变化已经得到了很好的证实。自Warburg假说提出以来，人们对如何激活这些代谢变化有了大量的了解。可引起癌症代谢变化的两种最常见的遗传改变是导致C-Myc （Myc）和缺氧诱导因子1 （HIF-1）转录因子激活的转化。这些转录因子改变了许多参与细胞代谢的基因，在致瘤环境中以牺牲正常细胞为代价促进癌细胞的增殖。治疗干预的一种方法是针对这些变化，以限制癌细胞？S的扩张和存活潜力相对于正常细胞。所附提案的假设是，工程方法将有助于提供对代谢转化的更定量的理解，可用于确定抑制癌细胞生长和存活的目标策略。这些工程目标将通过以下三个目标来实现：第一步将建立和调整包括Myc和HIF-1转化在内的癌症代谢动力学模型，以确定癌症治疗的潜在靶点。这种动力学建模方法将是有利的，因为它可以用来预测伴随Myc和HIF-1在癌细胞中引发的变化的酶活性和代谢率的变化。此外，由于潜在的癌症药物通常作用于代谢途径中的酶，因此这种动力学模型将有助于确定改变代谢的有用治疗方案。动力学模型将使用户能够检查许多不同的处理方案，这将是不可能重现实验。第二个目标将是利用这些数学模型的预测来修改癌细胞系统，以确定模型预测潜在癌症治疗能力的能力。这种方法将应用于一个模型癌细胞系，P493，代表伯基特？淋巴瘤。这种细胞系将特别合适，因为它具有控制Myc和HIF-1表达的能力。在适当的情况下，可以使用其他癌细胞系，以查看方法是否普遍适用。最终目的是评估模型癌细胞在接受和不接受治疗时的代谢物分布和代谢通量。代谢物标记将用于生成代谢通量框架，阐明癌症和治疗引起的变化。这一步的目的是确定模型预测和实验评估的变化是否在癌症代谢生理学的总体通量图中被观察到。这种定量评估将有助于更好地表征癌症代谢和治疗方法，也将有助于告知和改进癌症代谢的动力学模型。更广泛的影响：癌症是美国第二大死因，因此预防其致死率的合理方法是一个主要的健康目标。癌症代谢的改变存在于许多癌症中，包括b细胞淋巴瘤、白血病、胶质瘤、乳腺癌、肾癌等。通过应用工程方法，本研究将提供对癌细胞代谢如何变化的定量理解。这些知识将有助于开发更好的治疗策略，以抑制肿瘤生长，并可能激活细胞死亡。该项目还将开发一个新陈代谢建模模块，用于刺激和激发中学生对与人类健康相关的科学和工程的兴趣。本项目通过研究、教育和推广活动培养的学生将通过了解哺乳动物代谢并应用这些知识来改变哺乳动物细胞在生物技术和生物医学等领域的表现，对社会做出重要贡献。

Intellectual Merit: It has been well established since the work of Warburg that cancer cells exhibit changes in their cellular metabolism. In the years since the Warburg hypothesis, a great deal has been learned about how these metabolic changes can be activated. Two of the most common genetic alterations that can cause changes in cancer metabolism are transformations that lead to activation of the C-Myc (Myc) and hypoxia inducible factor 1 (HIF-1) transcription factors. These transcription factors alter many genes involved in cellular metabolism to facilitate the expansion of cancer cells at the expense of normal cells in tumorigenic environments. One approach for therapeutic intervention is to target these changes in order to limit a cancer cell?s expansion and survival potential relative to normal cells. The hypothesis of the enclosed proposal is that engineering methodologies will help provide a more quantitative understanding of the metabolic transformations that can be used to identify strategies to target for inhibiting the growth and survival of cancer cells. These engineering goals will be accomplished through the following three aims: The first step will be to build and adapt a kinetic model of cancer metabolism including Myc and HIF-1 transformations in order to identify potential targets for cancer treatment. Such a kinetic modeling approach will be advantageous since it can be used to predict changes in enzyme activity and metabolic rates that accompany the changes triggered by Myc and HIF-1 in cancer cells. Furthermore, such a kinetic model will be helpful in identifying useful treatment scenarios that alter metabolism since potential cancer drugs often act on an enzyme in the metabolic pathway. The kinetic model will enable users to examine many different treatment scenarios that would be impossible to reproduce experimentally. The second aim will be to modify cancer cell systems using the predictions of these mathematical models in order to determine the capacity of the model to predict potential cancer therapy capabilities. This approach will be applied to a model cancer cell line, P493, representing Burkitt?s lymphoma. This cell line will be particularly appropriate since it offers the ability to control expression of Myc and HIF-1. Other cancer cell lines are available if appropriate to see if the methodologies are generally applicable. The final aim will be to evaluate the metabolite profiles and metabolic flux of model cancer cells with and without treatment. Metabolite labeling will be applied to generate a metabolic flux framework elucidating changes resulting from cancer and treatments. The goal of this step is to determine if the alterations predicted by the model and evaluated experimentally are observed in an overall flux map of cancer metabolic physiology. This quantitative evaluation will help to better characterize cancer metabolism and treatment approaches and will also help to inform and improve kinetic models of cancer metabolism. Broader Impacts: Cancer represents the second leading cause of death in the US and thus rational approaches to prevent its lethality are a major health goal. Alterations in cancer metabolism are present in many cancers ranging from B-cell lymphomas, leukemias, gliomas, breast cancer, to renal carcinomas among others. By applying engineering approaches, this study will provide a quantitative understanding of how metabolism is changed in cancer cells. This knowledge will be useful in developing better treatment strategies that inhibit tumor growth and perhaps activate cell death. The project will also develop a metabolism modeling module that will be used to stimulate and excite middle school students about science and engineering related to human health. Students educated in this project through research, education and outreach initiatives will be important contributors to society by understanding mammalian metabolism and applying this knowledge to alter performance of mammalian cells in topics ranging from biotechnology to biomedicine.