A Computational Metabolomics tool (CoMet) for cancer metabolism

用于癌症代谢的计算代谢组学工具 (CoMet)

基本信息

批准号：
8474727
负责人：
JEFFREY SKOLNICK
金额：
$ 15.61万
依托单位：
GEORGIA INSTITUTE OF TECHNOLOGY
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-06-01 至 2015-05-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8474727
关键词：
Adverse effects Affect Algorithms Allosteric Regulation Antineoplastic Agents Behavior Biochemical Pathway Biological Cancer Patient Cancer cell line Cancerous Cell Line Cell physiology Cells Computational algorithm Computer Simulation Coupled Data Data Set Databases Development Disease Progression Drug Targeting Enzymes Gas Chromatography Goals Growth Human Investigation Jurkat Cells Ligands Malignant Neoplasms Mass Spectrum Analysis Measurement Measures Metabolic Metabolic Pathway Metabolism Methodology Methods Modeling Names Normal Cell Normal tissue morphology Play Proteins Proteome Quality of life Role Second Messenger Systems Structure Supplementation System Techniques Testing Therapeutic Update Validation Warburg Effect Work base cancer cell cancer therapy cancer type combinatorial computerized tools drug development enzyme structure improved lymphoblast metabolomics mortality novel therapeutics prevent research study second messenger small molecule therapeutic target tool tumor metabolism two-dimensional

项目摘要

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.

描述（由申请人提供）：这项工作的目的是创建，验证和应用硅模型和工具以预测在癌症中差异积累的代谢产物。我知道，代谢物可以广泛影响细胞行为和生物合成中间体的角色之外的生长，并且代谢越来越被认为是癌症治疗剂的潜在靶标。我们认为，癌细胞中某些代谢产物浓度的变化可能在疾病的进展中起积极作用，而不仅仅是其他变化的副作用或后果，以便预测这些变化的能力可能导致发展为代谢的癌症治疗的全新途径的发展。我们已经开始开发一种名为Comet的硅模型和工具，以做出这样的预测。在使用淋巴细胞的初步工作中，彗星成功地鉴定了抗增殖代谢物，尽管其代谢物水平的预测准确性及其代谢水平的准确性适用于其他类型的癌症，这是不确定的。为此，该提案I的第一个目的是通过整合详细的生物学数据并使用实验验证结果来改进其预测，这是I的第一个目的。为了执行我们的实验验证，我们将使用尖端的分析技术（二维气相色谱法耦合到质谱法或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。我们的第二个目的是测试彗星在多种类型的癌症中对下调和抗增生代谢物的预测的有效性，并使用这些结果进一步完善彗星的方法。我们的最终目的是衡量使用代谢物作为抗增生抗性的代谢影响，因为我们怀疑它们对细胞代谢有重大影响。这将使我们能够对其作用机理产生假设。这项工作是迈向对正常细胞和癌细胞之间的代谢差异以及代谢物代谢物在癌症增殖和进展中起作用的调节作用的重要一步。预测和理解这些变化将允许靶向癌症代谢的药物的合理发展，并提高代谢物本身作为抗癌剂的想法。通过攻击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的长期影响。