Integrative network modeling of regulatory modules in Large Granular Lymphocyte Leukemia

这个项目的广泛的长期目标是阐明监管机制 有助于癌症的发病机制。通过了解癌症如何以及为什么失调， 我们希望开发更好的治疗方法和设计更有效的治疗策略。我们实验室 研究一种叫做大颗粒淋巴细胞白血病的血癌。目前它是 认为白血病LGL细胞在以下情况下积累激活基因突变： 病理学上持续的炎症。这些激活的突变， 炎症信号传导导致这些LGL细胞的慢性增殖和扩增。这于是 以白色血细胞减少、脾脏肿大或 肝脏、骨髓疾病和/或自身免疫表现。目前没有可靠的治疗方法 并且大多数患者接受终身免疫抑制治疗。因此，我们希望继续 解决这一未满足需求的机制研究。通常，癌症具有不适当的激活， 主基因调节因子，如转录因子（TF）。像许多其他癌症一样，LGL白血病 细胞具有称为STAT3的TF的过度活化，30 - 40%的患者携带活化的 这个基因的突变。因为许多患者没有STAT3突变，而且因为STAT3 激活可能是健康免疫反应的一部分，我们推断可能还有其他因素 导致不适当的白血病增殖。因此，我们建议剖析监管 在LGL白血病中起作用的机制。在这项研究中，我们将描述发生在 基因组在多个调控水平，并将这些变化与基因表达编程 (Aim 1）。这将帮助我们确定在这种疾病中重要的调节模块，以及 以个性化的方式对比患者样本之间的功能差异。在 此外，我们将整合一个显着基因信号网络的计算模型， 根据我们的实验结果和现有的科学知识（目标2）。通过利用这个 网络模型，我们希望确定关键节点或图案的监管网络， 对疾病进展很重要，因此确定药物治疗关键靶点 干预如果成功，该项目的研究结果预计将有助于 LGL白血病的新治疗策略，更广泛地说， 失调

The broad long term objectives of this project are to elucidate the regulatory mechanisms contributing to the pathogenesis of cancer. By understanding how and why cancers are dysregulated, we hope to develop better therapeutics and design more effective treatment strategies. Our lab studies a blood cancer called large granular lymphocyte (LGL) leukemia. Currently it is thought that leukemic LGL cells accumulate activating genetic mutations in the context of pathologically persistent inflammation. These activating mutations, acting in concert with inflammatory signaling, lead to chronic proliferation and expansion of these LGL cells. This then contributes to clinical burden in the form of decreased white blood cells, enlarged spleen or liver, bone marrow disorders, and/or autoimmune manifestations. There is currently no reliable cure and most patients are managed on lifelong immunosuppressive therapy. Therefore, we hope to pursue mechanistic studies that address this unmet need. Often, cancers have inappropriate activation of master gene regulators such as transcription factors (TF). Like many other cancers, LGL Leukemia cells have hyper-activation of a TF called STAT3, with 30-40% of patients carrying an activating mutation in this gene. Because many patients do not have a STAT3 mutation, and because STAT3 activation can be part of a healthy immune response, we reasoned that there may be other factors leading to inappropriate leukemic proliferation. Therefore, we propose to dissect the regulatory mechanisms at play in LGL Leukemia. In this study, we will characterize changes that occur in the genome at multiple regulatory levels and correlate those changes with gene expression programming (Aim 1). This will help us identify regulatory modules important in this disease, as well as contrast functional differences between patient samples in a personalized way. In addition, we will integrate a computational model of the salient gene signaling network based on our experimental findings and existing scientific knowledge (Aim 2). By leveraging this network model, we expect to identify key nodes or motifs in the regulatory network that are important for disease progression, and therefore, identify critical targets for drug intervention. If successful, the findings from this project are expected to contribute to new treatment strategies in LGL leukemia and more broadly, to our understanding of cancer dysregulation.