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

学术价值：自从瓦尔堡的工作以来，癌细胞在其细胞代谢中表现出变化，这一点已经得到了很好的证实。 自瓦尔堡假说提出以来，人们对这些代谢变化是如何被激活的有了大量的了解。 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）转录因子活化的转化。 这些转录因子改变参与细胞代谢的许多基因，以促进癌细胞的扩增，而在致瘤环境中以正常细胞为代价。 治疗干预的一种方法是针对这些变化，以限制癌细胞？相对于正常细胞的扩增和存活潜力。 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，所述代谢转化可用于鉴定靶向抑制癌细胞生长和存活的策略。 这些工程目标将通过以下三个目标来实现：第一步将是建立和适应癌症代谢的动力学模型，包括Myc和HIF-1转化，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，因为它可以用于预测伴随癌细胞中Myc和HIF-1触发的变化的酶活性和代谢率的变化。 此外，这种动力学模型将有助于确定改变代谢的有用治疗方案，因为潜在的癌症药物通常作用于代谢途径中的酶。 动力学模型将使用户能够检查许多不同的治疗方案，这将是不可能重现实验。 第二个目标是使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。 这种方法将被应用到一个模型癌细胞系，P493，代表伯基特？s淋巴瘤。 该细胞系将是特别合适的，因为它提供了控制Myc和HIF-1表达的能力。 如果合适的话，还可以使用其他癌细胞系，看看这些方法是否普遍适用。 最终的目标是评估模型癌细胞在治疗和未治疗的情况下的代谢产物谱和代谢通量。 代谢物标记将被应用于生成阐明癌症和治疗引起的变化的代谢通量框架。 该步骤的目标是确定在癌症代谢生理学的总体通量图中是否观察到由模型预测和实验评估的改变。 这种定量评估将有助于更好地描述癌症代谢和治疗方法，也将有助于为癌症代谢的动力学模型提供信息和改进。更广泛的影响： 癌症是美国第二大死亡原因，因此预防其致命性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从B细胞淋巴瘤、白血病、神经胶质瘤、乳腺癌到肾癌等的许多癌症中。 通过应用工程方法，这项研究将提供对癌细胞中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，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有关的科学和工程的兴趣。 通过研究，教育和推广活动在这个项目中接受教育的学生将通过了解哺乳动物代谢和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主题中的表现，为社会做出重要贡献。

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.