Multiscale Modeling of Myelodysplastic Syndromes

骨髓增生异常综合征的多尺度建模

• 批准号: 9323833
• 负责人: Seth Joel Corey
• 金额: $ 72.97万
• 依托单位: VIRGINIA COMMONWEALTH UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-15 至 2020-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9323833
• 关键词: Acute Myelocytic Leukemia; Address; Affect; Age; Biological; Biological Assay; Blood; CSF3R gene; Cell Cycle Kinetics; Cell Death; Cells; Characteristics; Child; Chronic; Clinical; Clinical Management; Clinical Trials; Clonal Evolution; Clonality; Complex; Computational Biology; Computer Simulation; Cues; Cytometry; Data; Data Analyses; Data Set; Disease; Distal; Dose; Dysmyelopoietic Syndromes; Employee Strikes; Epidemiology; Event; Experimental Hematology; Exposure to; Flow Cytometry; Frequencies; Functional disorder; Gene Expression; Generations; Genes; Genetic Models; Germ-Line Mutation; Goals; Granulocyte Colony-Stimulating Factor; Granulocyte Colony-Stimulating Factor Receptors; Granulopoiesis; Growth; Hematological Disease; Hematopoiesis; Hematopoietic stem cells; Heterogeneity; Host Defense; Human; Ineffective Hematopoiesis; Infection; Inherited; Instruction; Intervention; JAK2 gene; Kinetics; Knowledge; Lead; Leukocyte Elastase; Life; Light; Malignant - descriptor; Measures; Mendelian disorder; Methods; Microarray Analysis; Modeling; Molecular; Mutation; Myelogenous; Nature; Network-based; Neutropenia; Noise; Nonsense Mutation; Outcome; Pathway Analysis; Patient risk; Patients; Pattern; Pharmacology; Phenotype; Phosphoproteins; Population; Population Genetics; Probability; Publishing; RUNX1 gene; Receptor Gene; Receptor Signaling; Recombinant Granulocyte Colony Stimulating Factor; Regulator Genes; Risk; Risk Assessment; Role; Signal Pathway; Signal Transduction; Stat5 protein; Stem cells; System; Systems Analysis; Techniques; Time; Transplantation; Treatment Efficacy; Validation; accurate diagnosis; actionable mutation; base; cytopenia; data modeling; design; experimental analysis; experimental study; gene induction; genome sequencing; granulocyte; graph theory; high dimensionality; induced pluripotent stem cell; innovation; insight; mathematical model; multi-scale modeling; multidisciplinary; mutant; network models; novel; prevent; progenitor; public health relevance; response; transcription factor; whole genome

 DESCRIPTION (provided by applicant): The granulocyte is absolutely essential for host defense and survival. Its pathophysiological importance is apparent in severe congenital neutropenia (SCN). Life-threatening infections in children with SCN can be avoided through the use of recombinant granulocyte colony-stimulating factor (GCSF), which increases the number of granulocytes. However, SCN often transforms into myelodysplastic syndrome (MDS) or acute myeloid leukemia (AML). A great unresolved clinical question is: do chronic, pharmacologic doses of GCSF contribute to this transformation. Two major sets of human clinical and experimental data strongly suggest such a linkage. First, a number of epidemiological clinical trials have demonstrated a strong association between exposure to GCSF and MDS/AML. Second, mutations in the distal domain of the GCSF Receptor (GCSFR) have been isolated from 70% of patients with SCN who developed MDS/AML. Most recently, clonal evolution over ~20 years was documented in a patient with SCN who developed MDS/AML. What is very striking is that five different mutations arose in the GCSFR gene, one persisted into the AML clone but others became extinct during the course. We hypothesize that clonal evolution of an SCN sick stem cell involves perturbations in proximal and distal signaling networks triggered by a mutant GCSFR. Transition from SCNMDSAML most likely also depends on chance, hence the need for a stochastic model. To address these hypotheses through computational modeling and experimental validation, we propose the following specific aims: Aim 1) Develop and evaluate a network model to account for the dynamics of normal and aberrant GCSFR signaling effects and their interactions with mutant ELANE; and Aim 2) Estimate the number, timing, and selective advantage of mutations in granulocyte progenitors at the MDS/AML stages and develop and validate population genetics models to predict risk of transition from SCNMDSAML. To accomplish these aims, we have assembled an expert multidisciplinary team in state-of-the-art experimental hematology (high-dimensional mass cytometry, cellular barcoding, and patient-derived iPSC), computational biology, network analysis, and applied probability to develop an innovative multiscale systems analysis of how defective granulopoiesis undergoes malignant transformation. Our goal is to produce a first-generation, multi-scale model for clonal evolution of a sick blood stem cell into an unstable one. Our long-term objectives are to establish patterns of network perturbations in myeloid clonal evolution, predict patient risk fo transformation, and design measures to prevent that life-threatening event. One insight from our modeling is that we can predict when transformation to MDS can occur in patients with SCN, which could be used to optimize surveillance and clinical intervention.

 描述（由申请人提供）：粒细胞对于宿主的防御和生存是绝对必要的。其病理生理学的重要性是明显的严重先天性中性粒细胞减少症（SCN）。通过使用重组粒细胞集落刺激因子（GCSF），可以避免SCN儿童中危及生命的感染，GCSF可以增加粒细胞的数量。然而，SCN经常转化为骨髓增生异常综合征（MDS）或急性髓性白血病（AML）。一个尚未解决的临床问题是：长期的，药理学剂量的GCSF有助于这种转化。两组主要的人类临床和实验数据强烈表明了这种联系。首先，许多流行病学临床试验已证明暴露于GCSF和MDS/AML之间存在强相关性。第二，GCSF受体（GCSFR）远端结构域的突变已从70%发生MDS/AML的SCN患者中分离出来。最近，在发生MDS/AML的SCN患者中记录了约20年的克隆进化。令人惊讶的是，GCSFR基因中出现了五种不同的突变，其中一种持续存在于AML克隆中，但其他突变在此过程中消失了。我们假设SCN患病干细胞的克隆进化涉及由突变GCSFR触发的近端和远端信号网络的扰动。从SCN → MDS → AML的转变很可能也取决于机会，因此需要一个随机模型。为了通过计算建模和实验验证来解决这些假设，我们提出了以下具体目标：目的1）开发和评估一个网络模型来解释正常和异常GCSFR信号效应的动态及其与突变ELANE的相互作用;和目标2）估计数量，时间，和粒细胞祖细胞突变在MDS/AML阶段的选择性优势，并开发和验证群体遗传学模型，以预测从SCN → MDS → AML转变的风险。为了实现这些目标，我们组建了一个专家多学科团队，他们在最先进的实验血液学（高维质量细胞计数，细胞条形码和患者来源的iPSC），计算生物学，网络分析和应用概率方面开发了一个创新的多尺度系统分析缺陷性粒细胞生成如何发生恶性转化。我们的目标是建立第一代多尺度模型，用于将病态血液干细胞克隆进化为不稳定的干细胞。我们的长期目标是建立髓系克隆进化中的网络扰动模式，预测患者转化的风险，并设计措施来预防危及生命的事件。从我们的建模中获得的一个见解是，我们可以预测SCN患者何时会发生MDS转化，这可用于优化监测和临床干预。