Multiscale Modeling of Myelodysplastic Syndromes

骨髓增生异常综合征的多尺度建模

• 批准号: 9144830
• 负责人: Seth Joel Corey
• 金额: $ 74.9万
• 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/9144830
• 关键词: Accounting; 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 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; Health; Hematological Disease; Hematopoiesis; Hematopoietic stem cells; Heterogeneity; Host Defense; Human; Ineffective Hematopoiesis; Infection; Inherited; 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; 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; Staging; Stat5 protein; Stem cells; System; Systems Analysis; Techniques; Therapeutic; Time; Transplantation; Validation; accurate diagnosis; actionable mutation; base; cytopenia; data modeling; design; gene induction; genome sequencing; granulocyte; graph theory; induced pluripotent stem cell; innovation; insight; mathematical model; multi-scale modeling; multidisciplinary; mutant; network models; novel; prevent; progenitor; research study; 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.