Comprehensive Informatic Analyses of AML Genomes and Epigenomes

摘要 在过去的十年中，基因组研究揭示了急性髓系白血病中出现的突变景观， 白血病（AML）。在每种情况下，这些突变中的一个甚至代表该肿瘤的起始。发起 当突变发生在造血干/祖细胞（HSPC）中时， 导致克隆造血，这是一种增加白血病转化可能性的状态。虽然我们 已经描述了这些起始突变的一些后果，包括转录变化， DNMT 3A突变的局灶性低甲基化表型，我们对这些变化的理解 反洗钱工作在很大程度上是不完整的。因为这些起始事件发生在肿瘤的每个细胞中，不像后来- 获得的亚克隆事件，它们也是有吸引力的治疗靶点。因此，本研究的第一个目标 该计划旨在确定引发突变导致AML的分子机制，并使用 利用这些信息来开发新的分子靶向疗法。我的任务是从大规模的基因组中 和表观基因组数据集转化为直观的，可理解的模型，生成关于 细胞转化为AML的过程。这样做将需要创造性的算法开发， 统计建模，生物信息学领域，我精通。这些知识可以引导我们 优先考虑新疗法的目标和方法。 我们研究项目的第二个主题是确定AML进展的原因， 突变。大多数起始突变本身不足以产生明显的AML，但大约5%的AML 病例似乎没有明显的协同突变。因此，我们将使用新技术， 分析方法，以搜索“暗物质”的基因组AML相关的基因组和 表观基因组变化，使用长读序来查询基因组的先前无法解析的部分， 全基因组测序，探索非编码区和结构变异，以及算法开发 以揭示AML中难以确定的事件。 由于我的跨学科背景，我在设计算法和统计模型方面具有专业知识， 以及对AML生物学的深刻理解。我的培训，经验，和生产记录， 有影响力的研究使我特别适合推动这项研究的信息学和分析方面 节目向前。

Abstract Over the last decade, genomic studies have revealed the landscape of mutations that appear in Acute Myeloid Leukemias (AML). In each case, one of these mutations represents the initiating even for that tumor. Initiating mutations can create a fitness advantage when they occur in hematopoietic stem/progenitor cells (HSPC), resulting in clonal hematopoeisis, a state that increases the likelihood of leukemic transformation. Although we have characterized some consequences of these initiating mutations, including transcriptional changes and focal hypomethylation phenotypes for the DNMT3A mutations, our understanding of how these changes promote AML is largely incomplete. Since these initiating events occur in every cell of the tumor, unlike later- acquired subclonal events, they also are attractive targets for therapy. Thus, the first goal of this research program is to define the molecular mechanisms by which initiating mutations cause AML, and to use this information to develop novel, molecularly-targeted therapies. My role will be to distill large genomic and epigenomic datasets into intuitive, comprehensible models, generating testable hypotheses about the process of cellular transformation into AML. Doing so will require creative algorithmic development and statistical modeling, areas of bioinformatics in which I am proficient. This knowledge can then guide us in prioritizing targets and approaches for novel therapeutics. A second theme of our research program is to identify the reasons for progression of AMLs with "missing" mutations. Most initiating mutations are insufficient to produce overt AML on their own, but about 5% of AML cases appear to have no clear cooperating mutations. We will therefore use new technologies and analytical approaches to search the "dark matter" of the genome for AML-relevant genomic and epigenomic changes, using long-read sequencing to query previously unresolvable portions of the genome, whole genome sequencing to explore non-coding regions and structural variation, and algorithmic development to reveal difficult to ascertain events in AML. Because of my interdisciplinary background, I have expertise in designing algorithms and statistical models, as well as a deep understanding of the biology of AML. My training, experience, and record of productive and impactful research make me uniquely suited to push the informatics and analysis aspects of this research program forward.