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.

项目总结 癌症最初是一种基因组疾病，DNA突变会引发一系列导致癌症的事件 进步。随着单个或少量的细胞集合经历状态转变而成为癌细胞，并最终 演变成恶性肿瘤，免疫系统被激活，新的血管形成，涉及 系统中的非癌细胞。这一过程涉及信息在多个 在时间和空间上进行缩放。信息在单元格内和跨多个单元格间编码和传输 基因组规模可以在系统的水平上被检测到。我们的假设是，包含在一个或多个 多基因组图谱可用于检测致癌干扰和预测治疗反应。它 已经证明，与急性髓细胞白血病相关的突变可以在疾病发作前数年检测到， 然而，他们不能预测疾病何时出现或对治疗有反应。然而，这些套装 突变的特征可以通过共同代表扰动的不同的基因表达签名来表征 以观察到的临床表型为基础。因此，迫切需要新颖和有洞察力的审讯。 以及在系统级别上对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量2)对数据简单节俭，3) 提供洞察和预测。我们建议验证一个数学模型和方法，该方法考虑到 从以下角度看从健康状态向癌症状态转变时全基因组的基因活性 信使RNAs(mRNAs；转录组)、非编码microRNA(miRNAs；miRoman)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界是众所周知的。 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。这一提议的中心假设是，在生物过程中产生的信息 例如癌症，可以从不同的角度(即转录组、微罗马、表观基因组)进行检测，从而 包含在基因组图景的一种观点中的信息可以映射到另一种观点中，而这种疾病 发展和进步可以用信息流的数学模型来解释和预测 一个多维的基因组空间。我们提出了以下目标：具体目标1.将一个 多维状态转换的数学模型。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。我们将对其进行量化和建模 受控急性髓系白血病小鼠模型的治疗反应。具体目标3.描述《公约》所载信息 转录组、微罗马和表观基因组状态空间。冲击力。通过一个迭代的对话 生物实验和数学建模，这项工作将提供对微扰造成的洞察 与白血病的启动和进展有关，这将指导针对关键途径的新疗法的设计 过渡点。