A Computational Metabolomics tool (CoMet) for cancer metabolism

描述(由申请人提供)：这项工作的目标是创建、验证和应用计算机模型和工具来预测癌症中不同积累的代谢物。众所周知，代谢物除了作为生物合成中间体的作用外，还可以广泛影响细胞的行为和生长，代谢越来越被认为是癌症治疗的潜在靶点。我们认为，癌细胞中某些代谢物浓度的变化可能在疾病的进展中发挥积极作用，而不仅仅是其他变化的副作用或结果，因此预测这些变化的能力可能导致开发全新的以新陈代谢为重点的癌症治疗途径。我们已经开始开发一种名为彗星的计算机模型和工具来做出这样的预测。在使用淋巴母细胞的初步工作中，彗星已经成功地识别出抗增殖代谢物，尽管它对代谢物水平预测的准确性，以及它的 对其他类型癌症的适用性尚不确定。为此，这项建议的第一个目标是通过整合详细的生物学数据并使用实验验证结果来完善其预测来改进彗星。为了执行我们的实验验证，我们将使用一种尖端分析技术(二维气相色谱-质谱仪，或GCxGC-MS)来测量癌细胞和正常细胞中代谢物的水平，并将这些结果与彗星的预测进行比较。我们的第二个目标是测试Comet对多种癌症中下调和抗增殖代谢物预测的有效性，并利用这些结果进一步完善Comet的方法。我们的最终目标是衡量使用代谢物作为抗增殖剂的代谢影响，因为我们怀疑它们对细胞新陈代谢有实质性影响。这将使我们能够对它们的行动机制产生假设。这项工作是朝着获得对正常细胞和癌细胞之间的代谢差异以及代谢产物在癌症增殖和进展中所起的调节作用的预测性了解迈出的重要一步。预测和了解这些变化将允许合理开发针对癌症新陈代谢的药物，并促进代谢物本身作为抗癌剂的想法。通过攻克癌症的这一基本方面，这项工作可能会对癌症死亡率和癌症患者的生活质量产生重大而广泛的长期影响。 公共卫生相关性：这项工作旨在开发一种模型和计算工具来预测哪些代谢中间产物在癌细胞中积累或耗尽。预测和了解这些变化将允许有针对性地开发攻击癌症新陈代谢的药物，从而限制癌症的生长和进展。此外，这些分子本身可能扮演着天然抗癌剂的角色。

DESCRIPTION (provided by applicant): The goal of this work is to create, validate, and apply an in silico model and tool to predict metabolites that are differentially accumulated in cancer. I is known that metabolites can broadly impact cellular behavior and growth outside of their roles as biosynthetic intermediates, and metabolism is being increasingly recognized as a potential target for cancer therapeutics. We believe that changes in concentration of some metabolites in cancer cells may have an active role in the progression of the disease rather than being just a side effect or consequence of other changes, such that the ability to predict these changes could result in the development of entirely new avenues of metabolism-focused cancer treatment. We have begun to develop an in silico model and tool, named CoMet, to make such predictions. In preliminary work using lymphoblasts, CoMet has successfully identified antiproliferative metabolites, though the accuracy of its predictions of metabolite levels, and its applicability to other types of cancer, is uncertain. To this end, the first aim of this proposal i to improve CoMet by integrating detailed biological data and using experimental validation results to refine its predictions. To perform our experimental validations, we will use a cutting-edge analytical technique (two-dimensional gas chromatography coupled to mass spectrometry, or GCxGC-MS) to measure the levels of metabolites in cancerous and normal cells and compare these results to predictions made by CoMet. Our second aim is to test the validity of CoMet's predictions of down-regulated and antiproliferative metabolites in multiple types of cancer, and to use these results to further refine CoMet's methodology. Our final aim is to measure the metabolic impact of using metabolites as antiproliferatives, since we suspect that they are having a substantial impact on cellular metabolism. This will allow us to generate hypotheses on their mechanisms of action. This work is a significant step towards gaining a predictive understanding of the metabolic differences between normal and cancerous cells, and of the regulatory roles metabolites play in cancer proliferation and progression. Predicting and understanding these changes would allow for the rational development of drugs that target cancer metabolism, and for advancement of the idea of metabolites that themselves serve as anticancer agents. By attacking such a fundamental aspect of cancer, this work could have a significant and broad long-term impact on cancer mortality and the quality of life of cancer patients. PUBLIC HEALTH RELEVANCE: This work aims to develop a model and computational tool to predict which metabolism intermediates are accumulated or depleted in cancerous cells. Predicting and understanding these changes would allow for the targeted development of drugs that attack cancer metabolism and thus limit cancer growth and progression. Additionally, these molecules may themselves play roles as natural anticancer agents.