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: 10392361
• 负责人: YA-HUEI KUO
• 金额: $ 86.32万
• 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/10392361
• 关键词: 1q22; Acute Myelocytic Leukemia; Behavior; Biological; Biological Process; CBFbeta-MYH11 fusion protein; Cancer Biology; Cells; Chromosomal Rearrangement; Clinic; Collection; Communities; Complex; DNA; DNA Methylation; DNA Sequence Alteration; Data; Development; Disease; Disease Progression; Epigenetic Process; Event; Evolution; Explosion; Gene Expression Profile; Genes; Genome; Genomics; Goals; Health; Hematopoiesis; Human; Immune system; Institution; Lead; 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; Probability; Process; Published Comment; RNA; Relapse; Research; Rest; Sampling; Series; System; Systems Biology; Time; Transgenes; Untranslated RNA; Work; base; 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; 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 癌症是作为基因组疾病开始的，DNA突变引发了一系列导致癌症的事件 进展。随着单个或少量细胞的收集发生状态转变，成为癌细胞，最终会成为癌细胞 演变成恶性肿瘤，免疫系统被激活并形成新的脉管系统，涉及 系统中的非癌细胞。此过程涉及信息流的流量和传输 时间和空间缩放。信息在单元格之间并在多个细胞之间进行编码并传输 可以在系统级别检测到基因组量表。我们的假设是一个或 多种基因组景观可用于检测致癌性扰动并预测对治疗的反应。它 已显示，与AML相关的突变可以在疾病发作之前几年检测到 但是，他们无法预测疾病何时会表现或对治疗的反应。然而，这些套装 突变的特征可以以不同的基因表达特征共同表示扰动 观察到的临床表型的基础。那是迫切需要小说和有见地的审讯 以及系统级别上高维基因组数据集的预测。我们的方法旨在1）利用 系统中的最大相关信息数量2）简单且与数据相提并论，3） 提供洞察力和预测。我们建议验证一种考虑的数学模型和方法 全基因组基因活性是从健康状态到癌症状态的状态过渡 Messenger RNA（mRNA；转录组），非编码microRNA（mirnas; mirome）和DNA甲基化 （表观基因组）。国家过渡的理论和数学在系统生物学社区中众所周知 它是解释和预测复杂系统（例如基因组学和）行为的强大工具 癌症生物学。该提议的核心假设是生物过程中产生的信息 例如，可以从不同的观点（即转录组，mi象，表观基因组）检测到癌症 基因组景观的一个观点中包含的信息可以映射到另一种疾病中 可以通过信息流的数学模型来解释和预测发展和进步 多维基因组空间。我们提出以下目的：特定目标1。参数化 多维状态过渡的数学模型。特定目标2。量化治疗对 状态过渡动态并开发一种治疗反应和继电器的模型。我们将量化和建模 受控AML小鼠模型中的治疗反应。特定目的3。表征包含的信息 转录组，米我和表观基因组状态空间。影响。通过之间的迭代对话 生物实验和数学建模，这项工作将提供有关扰动的见解 为白血病倡议和进步，这将指导针对关键途径的新疗法的设计 过渡点。