Information flow and state transitions at the system and multi-dimensional scales in leukemia progression

白血病进展中系统和多维尺度的信息流和状态转换

• 批准号: 10625292
• 负责人: YA-HUEI KUO
• 金额: $ 70.06万
• 依托单位: BECKMAN RESEARCH INSTITUTE/CITY OF HOPE
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10625292
• 关键词: 1q22; Acute Myelocytic Leukemia; Biological; Biological Process; CBFbeta-MYH11 fusion protein; Cancer Biology; Cells; Chromosomal Rearrangement; Clinic; Collection; Communities; Complex; DNA; DNA Methylation; DNA Sequence Alteration; Data; Development; Dimensions; Disease; Disease Progression; Epigenetic Process; Event; Evolution; Experimental Models; Explosion; Gene Expression Profile; Genes; Genome; Genomics; Goals; Health; Hematopoiesis; Human; Immune system; Institution; Leukemic Cell; Malignant - descriptor; Malignant Neoplasms; Maps; Mathematics; Measures; Messenger RNA; Methylation; MicroRNAs; Modeling; Modification; Mutation; Oncogenic; Onset of illness; Pathogenesis; Pathway interactions; Patients; Potential Energy; Prediction of Response to Therapy; Probability; Process; Published Comment; RNA; Relapse; Research; Rest; Sampling; Series; System; Systems Biology; Time; Transgenes; Untranslated RNA; Work; behavior prediction; biobank; cancer cell; cancer therapy; clinical phenotype; combinatorial; design; epigenome; experimental study; fusion gene; genome-wide; genomic data; geometric methodologies; high dimensionality; information model; insight; leukemia; leukemia treatment; mathematical methods; mathematical model; mathematical theory; mouse model; new therapeutic target; novel; peripheral blood; personalized medicine; predicting response; targeted treatment; theories; tool; transcriptome; treatment response; tumor progression

PROJECT SUMMARY Cancer begins as a disease of the genome, with DNA mutations initiating a cascade of events that lead to cancer progression. As single or small collection of cells undergo state transitions to become cancer cells and ultimately evolve into a malignant neoplasm, the immune system is activated and new vasculature is formed, involving non-cancerous cells in the system. This process involves the flow and transfer of information across multiple scales in time and space. Information is encoded within and transferred between cells and across multiple genomic scales may be detected at the system’s level. Our hypothesis is that information contained in one or multiple genomic landscapes can be used to detect oncogenic perturbations and predict response to therapy. It has been shown that mutations associated with AML can be detected years before the onset of disease, however, they do not predict when the disease will manifest or response to treatment. Nevertheless, these sets of mutations can be characterized by distinct gene expression signatures collectively representing perturbations underlying the observed clinical phenotypes. Thus, there is an urgent need for novel and insightful interrogations and predictions of high-dimensional genomic data sets on a system level. Our approach aims to 1) make use of the maximum amount of relevant information in the system 2) be simple and parsimonious with the data, and 3) provide insight and predictions. We propose to validate a mathematical model and approach that considers genome-wide gene activity as state transition from a healthy state to a cancer state from the perspectives of messenger RNAs (mRNAs; transcriptome), non-coding microRNA (miRNAs; the miRome), and DNA methylation (epigenome). The theory and mathematics of state transitions is well known in the systems biology community and is a powerful tool for interpreting and predicting the behavior of complex systems such as genomics and cancer biology. The central hypothesis of this proposal is that information produced during a biological process such as cancer, can be detected from different viewpoints (i.e., transcriptome, miRome, epigenome) such that information contained in one viewpoint of the genomic landscape can be mapped into another, and that disease development and progression can be interpreted and predicted with mathematical models of information flow in a multidimensional genomic space. We propose the following aims: Specific Aim 1. Parameterize a mathematical model of multi-dimensional state transition. Specific Aim 2. Quantify the impact of treatment on state transition dynamics and develop a model of therapy response and relapse. We will quantify and model therapy response in controlled AML mouse model. Specific Aim 3. Characterize the information contained in the transcriptome, miRome, and epigenome state-spaces. Impact. Through an iterative dialog between biological experiments and mathematical modeling, this work will provide insight into perturbations contributing to leukemia initiation and progression, which will guide the design of new therapies targeting pathways at critical transition points.

项目摘要 癌症始于基因组疾病，DNA突变引发一系列导致癌症的事件 进展当单个或小的细胞集合经历状态转变成为癌细胞， 发展成恶性肿瘤时，免疫系统被激活，形成新的血管， 系统中的非癌细胞。此过程涉及多个站点之间的信息流动和传输 时间和空间的尺度。信息在细胞内编码，并在细胞之间传输， 可以在系统水平上检测基因组规模。我们的假设是，包含在一个或 多个基因组景观可用于检测致癌干扰和预测对治疗的反应。它 已经表明，与AML相关的突变可以在疾病发作前数年检测到， 然而，它们不能预测疾病何时出现或对治疗的反应。然而，这些集 突变的特征可以是不同的基因表达特征，共同代表扰动 作为观察到的临床表型的基础。因此，迫切需要新颖而富有洞察力的审讯 以及在系统水平上预测高维基因组数据集。我们的方法旨在1）利用 系统中相关信息的最大量2）简单且与数据节约，以及3） 提供洞察力和预测。我们建议验证一个数学模型和方法，考虑 全基因组基因活性作为从健康状态到癌症状态的状态转变， 信使RNA（mRNA;转录组），非编码microRNA（miRNAs; miRome）和DNA甲基化 （表观基因组）。状态转换的理论和数学在系统生物学社区中是众所周知的 是解释和预测复杂系统行为的有力工具，如基因组学和 癌症生物学这一提议的核心假设是，在生物过程中产生的信息 例如癌症，可以从不同的视角检测（即，转录组、微基因组、表观基因组）， 包含在基因组景观的一个观点中的信息可以映射到另一个观点， 发展和进步可以用信息流的数学模型来解释和预测， 多维基因组空间。我们提出以下目标：具体目标1。旁叶藓属 多维状态转移的数学模型。具体目标2。量化治疗对 状态转换动力学并开发治疗反应和复发的模型。我们将量化和建模 对照AML小鼠模型中的治疗反应。具体目标3。描述 转录组、微基因组和表观基因组状态空间。冲击通过一个反复的对话， 生物实验和数学建模，这项工作将提供洞察扰动贡献 这将指导针对关键通路的新疗法的设计， 过渡点。