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、验证和应用一个计算机模型和工具来预测癌症中代谢物的差异积累。众所周知，代谢物在作为生物合成中间体的作用之外，可以广泛地影响细胞行为和生长，并且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后果，因此预测这些变化的能力可能会导致以代谢为重点的癌症治疗的全新途径的发展。我们已经开始开发一个名为CoMet的计算机模型和工具来进行这样的预测。在使用淋巴母细胞的初步工作中，CoMet已经成功地确定了抗增殖代谢物，尽管它对代谢物水平的预测的准确性，以及它的准确性

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.