Characterization of the functions of one-carbon metabolism across human cancers

人类癌症中一碳代谢功能的表征

• 批准号: 9410769
• 负责人: Mahya Mehrmohamadi
• 金额: $ 7.11万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-02-01 至 2021-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9410769
• 关键词: Algorithms; Anabolism; Biochemical Pathway; Cancer cell line; Cancerous; Carbon; Cells; Clinical; Clinical Data; Computer Simulation; Copy Number Polymorphism; DNA Methylation; DNA Methylation Regulation; Diet; Diet Records; Dietary Component; Dietary Intervention; Enzymes; Epigenetic Process; Equilibrium; Fluorouracil; Gene Expression; Genomic Segment; Genomics; Glycine; Goals; Heterogeneity; Human; Individual; Knowledge; Label; Lead; Link; Machine Learning; Malignant Neoplasms; Mathematics; Metabolic; Metabolic Pathway; Metabolism; Methionine; Methotrexate; Modeling; Molecular; Molecular Profiling; Outcome; Pathway interactions; Patients; Pattern; Pemetrexed; Physiological; Process; Route; Serine; Serum; Somatic Mutation; Source; Subgroup; Survival Rate; The Cancer Genome Atlas; Translating; Validation; Variant; base; cancer prevention; cancer therapy; cancer type; cell growth regulation; chemotherapeutic agent; chemotherapy; cohort; epigenetic regulation; epigenomics; experimental analysis; genomic predictors; genomic profiles; human subject; metabolomics; novel; precision medicine; predicting response; prediction algorithm; response; tumor

Project Summary/Abstract Cancerous cells reprogram their metabolism to accommodate their needs. A deep understanding of this metabolic reprograming can lead to novel and promising routes toward cancer treatment. Different human cancers differ with respect to aspects of their metabolic rewiring, however a profound knowledge in this field is lacking. The main goal of my thesis project is to characterize the ways in which one-carbon metabolism — a metabolic pathway highly altered in cancer— is utilized by different human cancers and elucidate its consequences on downstream processes such as epigenetics and biosynthesis. Using information in genomic profiles of individual tumors, I build computational models to infer metabolic landscapes, assess their implications in patient survival, and predict response to chemotherapy. By studying one-carbon metabolites in the human serum, I confirm the relevance of theses findings in human contexts and suggest potentials for dietary intervention approaches. In aim 1, I characterize the usage of serine through one-carbon metabolism across human cancers. I performed flux analysis using gene expression profiles of hundreds of human tumors, followed by experimental validation using metabolomics approaches. In aim 2, I identify the determinants of human serum methionine. Diet records, serum metabolomics, and clinical data from a cohort of human subjects were incorporated into computational models. The determinants of variability in serum methionine were then quantified, suggesting a mechanism for regulation of cellular epigenetics by the diet. In aim 3, I determine the sources of variation in DNA methylation across human cancers and the contribution of metabolism. I integrated molecular and clinical profiles of thousands of human tumors from the TCGA into machine-learning algorithms to identify their association with DNA methylation. A major contribution from one-carbon metabolism in regulating DNA methylation status in tumors was found. In aim 4, I predict response to anti-metabolic chemotherapies based on tumor genomics. I plan to translate my previous findings into clinical discoveries. To this end, I will demonstrate how tumor profiles can be used to model patient survival, predict response to chemotherapy, and move toward precision medicine.

项目概要/摘要 癌细胞重新编程其新陈代谢以满足其需求。 这种代谢重编程可以带来新颖且有希望的癌症治疗途径。 不同的人类癌症在代谢重连方面有所不同，但是 我的论文项目的主要目标是描述该领域的广泛知识。 一碳代谢（癌症中的一种高度代谢途径）的方式 被不同的人类癌症利用并阐明其对下游过程的影响 例如利用个体肿瘤基因组谱中的信息，我。 建立计算模型来推断代谢景观，评估其对患者的影响 通过研究中的一碳代谢物来预测生存率。 人血清，我证实了这些研究结果在人类环境中的相关性，并建议 在目标 1 中，我描述了丝氨酸的用途。 通过人类癌症的一碳代谢，我使用基因进行了通量分析。 数百种人类肿瘤的表达谱，然后使用以下方法进行实验验证 在目标 2 中，我确定了人血清蛋氨酸的决定因素。 来自一组人类受试者的饮食记录、血清代谢组学和临床数据 纳入计算模型。 然后进行量化，提出了饮食调节细胞表观遗传学的机制。 3、我确定了人类癌症中 DNA 甲基化变异的来源以及 我整合了数千名人类的分子和临床概况。 将肿瘤从 TCGA 转化为机器学习算法，以识别它们与 DNA 的关联 甲基化是一碳代谢在调节 DNA 甲基化方面的主要贡献。 在目标 4 中，我预测了抗代谢化疗的反应。 我计划将我之前的发现转化为临床发现。 为此，我将演示如何使用肿瘤概况来模拟患者生存、预测 对化疗的反应，并走向精准医疗。