Multiscale Modeling of Myelodysplastic Syndromes

 描述(申请人提供)：粒细胞对宿主防御和生存是绝对必要的。在严重的先天性中性粒细胞减少症(SCN)中，其病理生理学意义明显。通过使用重组粒细胞集落刺激因子(GCSF)，增加粒细胞的数量，可以避免儿童SCN中危及生命的感染。然而，SCN往往转化为骨髓增生异常综合征(MDS)或急性髓系白血病(AML)。一个巨大的临床悬而未决的问题是：长期的、药理剂量的GCSF是否有助于这种转变。两组主要的人类临床和实验数据有力地表明了这种联系。首先，一些流行病学临床试验表明，暴露于GCSF和MDS/AML之间存在很强的相关性。第二，GCSF受体(GCSFR)远端区域的突变已从70%发展为MDS/AML的SCN患者中分离出来。最近，1例SCN患者发展为MDS/AML，其克隆性进化超过20年。非常惊人的是，GCSFR基因出现了五种不同的突变，其中一种保留在AML克隆中，但其他的在过程中灭绝了。我们假设SCN病态干细胞的克隆进化涉及由突变的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。为了通过计算模型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，以解释正常和异常的GCSFR信号效应的动力学以及它们与突变的Elane的相互作用；以及目标2)估计粒系祖细胞在MDS/AML阶段的突变的数量、时机和选择优势，并开发和验证群体遗传学模型以预测从SCNMDSAML过渡的风险。为了实现这些目标，我们组建了一个多学科的专家团队，研究最先进的实验血液学(高维质量细胞术、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，以开发一种创新的多尺度系统分析缺陷粒系如何经历恶变。我们的目标是建立第一代多尺度模型，将患病的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，并设计预防这种危及生命的事件的措施。从我们的建模中得到的一个启示是，我们可以预测SCN患者何时可以转变为MDS，这可以用于优化监测和临床干预。

 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.