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: 9229841
• 负责人: Mahya Mehrmohamadi
• 金额: $ 3.22万
• 依托单位: CORNELL UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-21 至 2017-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9229841
• 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; 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; abstracting; base; cancer prevention; cancer therapy; cancer type; cell growth regulation; chemotherapeutic agent; chemotherapy; cohort; epigenomics; experimental analysis; genomic profiles; human subject; metabolomics; novel; precision medicine; predicting response; prediction algorithm; response; tumor; tumor metabolism

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.

项目摘要/摘要 癌细胞重新编程其新陈代谢，以适应他们的需求 这种代谢的重复性可以导致新颖和有希望的途径通往癌症的信任。 不同的人类癌症在其代谢重新布线方面有所不同，但是 我的论文项目的主要目标是在这个领域的深刻知识。 一碳代谢的方式 - A代谢途径在癌症中变化很高 由不同的人类癌症利用并阐明其在下游过程中的后果 例如，表观遗传学和生物合成。 建立计算模型来推断代谢景观，评估患者的含义 生存，并通过研究一个碳代谢物来预测化学疗法。 人血清，我证实了这些发现在人类背景下的相关性并暗示 饮食干预方法的潜力。 通过人类癌症的一碳代谢。 人类肿瘤的表达曲线，然后使用经验验证 代谢组学在AIM 2中，我确定了人类血清甲基接受的决定因素。 饮食记录，血清代谢组学和来自人类受试者队列的临床数据是 纳入计算模型。 然后进行量化，提出一种通过饮食来调节细胞表观遗传学的机制 3，我确定了人类癌和你的DNA甲基化变化的来源 代谢的贡献。 从TCGA到机器学习算法以识别其与DNA的关联 甲基化。 在AIM 4中发现了肿瘤的状态，我预测对抗代谢化疗的反应 根据肿瘤基因组学 这个目的，我将证明如何使用肿瘤特征来模拟患者的生存，预测 对化学疗法的反应，并转向精确医学。