Comprehensive Informatic Analyses of AML Genomes and Epigenomes

AML 基因组和表观基因组的综合信息分析

• 批准号: 10693348
• 负责人: Christopher A Miller
• 金额: $ 19.34万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-20 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10693348
• 关键词: Acute Myelocytic Leukemia; Algorithm Design; Area; Award; Big Data; Bioinformatics; Biology; Cell physiology; Creativeness; DNMT3a mutation; Data Set; Disease; Event; Genetic Transcription; Genome; Genomics; Goals; Hematopoietic stem cells; Informatics; Intuition; Knowledge; Modeling; Molecular; Mutation; Patients; Phenotype; Productivity; Research; Role; Statistical Models; Training; Untranslated RNA; Variant; algorithm development; computer program; dark matter; epigenome; epigenomics; experience; experimental study; fitness; genome sequencing; leukemic transformation; metaplastic cell transformation; molecular targeted therapies; neoplastic cell; new technology; novel; novel therapeutic intervention; novel therapeutics; programs; statistics; targeted treatment; tumor; whole genome

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.

抽象的 在过去的十年中，基因组研究揭示了出现在急性髓样中的突变的景观 白血病（AML）。在每种情况下，这些突变之一都代表了该肿瘤的启动。发起 突变在造血茎/祖细胞（HSPC）中发生时会产生健身优势， 导致克隆造血作用，这种状态增加了白血病转化的可能性。虽然我们 已经表征了这些启动突变的某些后果，包括转录变化和 DNMT3A突变的局灶性低甲基化表型，我们对这些变化的理解 促进AML在很大程度上是不完整的。由于这些启动事件发生在肿瘤的每个细胞中 获得了克隆的事件，它们也是治疗的有吸引力的靶标。因此，这项研究的第一个目标 程序是定义启动突变引起AML的分子机制，并使用 这些信息以开发出新颖的分子靶向疗法。我的角色是提炼大型基因组 和表观基因组数据集成直观，可理解的模型，生成有关该模型的可检验假设 细胞转化为AML的过程。这样做将需要创意算法开发和 统计建模，我精通生物信息学领域。然后，这些知识可以指导我们 优先考虑新型治疗剂的目标和方法。 我们的研究计划的第二个主题是确定AML进展的原因 突变。大多数启动突变不足以自行生产明显的AML，但约有5％的AML 案件似乎没有明确的合作突变。因此，我们将使用新技术， 搜索基因组的“暗物质”的分析方法，以进行AML相关的基因组和 表观基因组变化，使用长阅读测序来查询以前无法解决的基因组的部分， 全基因组测序以探索非编码区域和结构变化以及算法发展 揭示难以确定AML中的事件。 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，经验和生产力记录 有影响力的研究使我非常适合推动这项研究的信息学和分析方面 程序向前。

项目成果

Efficient Algorithms Unlock Understanding of Clonal Evolution in Cancer.

高效的算法解锁了对癌症克隆进化的理解。

• DOI: 10.1158/2643-3230.bcd-22-0036
• 发表时间: 2022
• 期刊: Blood cancer discovery
• 影响因子: 11.2
• 作者: Miller,ChristopherA

Discovery of a novel genomic alteration that renders leukemic cells resistant to CD19-targeted immunotherapies.

• DOI: 10.1182/bloodadvances.2022007705
• 发表时间: 2022-10-25
• 期刊: BLOOD ADVANCES
• 影响因子: 7.5
• 作者: Ghobadi, Armin;Landmann, Jack H.;Carter, Alun;Cooper, Matthew L.;Selli, Mehmet Emrah;Chang, Jufang;Baker, Matthew;Miller, Christopher A.;Ferraro, Francesca;Chen, David Y.;Smith, Amanda M.;LaValle, Taylor A.;Duncavage, Eric J.;Chou, Justin;Tam, Victor;Benoun, Joseph M.;Nater, Jenny;Scholler, Nathalie;Milletti, Francesca;Vezan, Remus;Bot, Adrian;Rossi, John M.;Singh, Nathan
• 通讯作者: Singh, Nathan