COMPREHENSIVE INFORMATIC ANALYSES OF AML GENOMES AND EPIGENOMES

AML 基因组和表观基因组的全面信息学分析

• 批准号: 10246931
• 负责人: Christopher A Miller
• 金额: $ 10.73万
• 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 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10246931
• 关键词: ATAC-seq; Acute Myelocytic Leukemia; Aftercare; Algorithms; Area; Award; Big Data; Bioinformatics; Biology; Biopsy; Clinical; Clinical Trials; Clonal Evolution; Couples; DNA Sequence Alteration; Data; Data Set; Development; Disease; Disease remission; Early Intervention; Event; Genome; Genomics; Goals; Growth; Hematopoietic stem cells; Informatics; Knowledge; Molecular; Morphology; Mutation; Patients; Primary Neoplasm; Relapse; Research; Residual Neoplasm; Role; Statistical Models; Testing; Training; Translating; Work; aggressive therapy; bisulfite sequencing; chemotherapy; complex data; computer program; deep sequencing; design; epigenomics; experience; experimental study; genome sequencing; leukemia; mouse model; novel therapeutics; prognostic; programs; relapse risk; single-cell RNA sequencing; statistics; tool; transcriptome sequencing; transcriptomics; tumor; whole genome

Abstract A decade of genomic studies has revealed the landscape of mutations appearing in the genomes of Acute Myeloid Leukemias (AML). A subset of these mutations have been identified as AML-initiating events that create growth advantages in a hematopoietic stem/progenitor cell (HSPC). This leads to the early stages of disease, but the mechanisms by which these mutations act is poorly understood. This research program will study key AML-initiating events in both primary tumors and mouse models by generating comprehensive whole-genome transcriptomic and epigenomic data (RNA-seq, bisulfite-seq, ATAC-seq, single-cell RNA-seq, et al). My role will be to translate these large, complex data sets into conclusions and testable hypotheses about the precise molecular mechanisms by which these specific genetic mutations initiate AML. Doing so will require the development of new algorithms and statistical models, both areas of bioinformatics in which I am proficient. We will then leverage this knowledge to develop novel therapies. A second key question focuses on the 50% of AML patients who initially experience complete remission after chemotherapy, but ultimately relapse. Our previous work has shown that genome sequencing can often identify persistent leukemia-associated mutations in post-chemotherapy biopsies from these patients, even when they are in morphological remission. This lack of mutation clearance was strongly associated with risk of relapse, but was not absolutely predictive: a few patients with persistent mutations experienced long remissions, and others who cleared all mutations quickly relapsed. Two remaining questions with important clinical implications are: 1) Can sequencing of biopsies from additional post-treatment timepoints better define the trajectory of mutation clearance and offer additional prognostically useful information? 2) Could ultra-deep sequencing have detected the residual disease responsible for relapse, thus prompting earlier interventions? Having co-led the original mutation clearance study, and authored tools for tracking a tumor's clonal evolution through therapy, I have the required expertise to deign these studies, and analyze, visualize, and interpret these data. We are beginning clinical trials that use clearance to assign patients to more or less aggressive treatments and providing more clarity on the above questions will be a key part of translating these findings into robust clinical tests. My interdisciplinary skillset couples a deep understanding of the biology of AML with statistical acumen and expertise in designing new algorithms. My training, experience, and record of successful scientific contributions make me uniquely suited to drive the informatics and analysis aspects of these projects forward.

抽象的 十年的基因组研究揭示了急性基因组中突变的景观 髓样白血病（AML）。这些突变的一个子集已被确定为AML发射事件 在造血干/祖细胞（HSPC）中创建生长优势。这导致了 疾病，但是这些突变作用的机制知之甚少。该研究计划将 通过产生全面的原发性肿瘤和小鼠模型中的关键AML发射事件 全基因组转录组和表观基因组数据（RNA-Seq，Bisulfite-Seq，Atac-Seq，单细胞RNA-Seq， 等）。我的角色是将这些大型的复杂数据集转化为结论和可检验的假设 关于这些特定遗传突变启动AML的精确分子机制。这样做会 需要开发新的算法和统计模型，这是我所在的生物信息学领域 精通。然后，我们将利用这些知识来开发新的疗法。 第二个关键问题重点是50％的AML患者，他们最初在 化学疗法，但最终复发。我们以前的工作表明，基因组测序通常可以 在这些患者的化学后活检中，确定与白血病相关的持续性白血病相关突变，甚至 当它们处于形态上的缓解时。缺乏突变清除率与 复发，但不是绝对预测的：一些持续突变的患者经历了很长的时间 减免，以及清除所有突变的其他人迅速复发。剩下的两个问题 临床意义是：1）可以从其他处理后时间点对活检进行测序更好地定义 突变清除的轨迹并提供其他预后有用的信息？ 2）可能非常深 测序已检测到负责复发的残留疾病，从而促进了较早的干预措施？ 共同领导的原始突变清除研究，并为跟踪肿瘤的克隆进化而撰写了工具 通过治疗，我拥有将这些研究取代，分析，可视化和解释所需的专业知识 这些数据。我们正在开始使用间隙将患者分配给或多或少侵略性的临床试验 治疗并在上述问题上提供更多清晰度将是翻译这些发现的关键部分 进入强大的临床测试。 我的跨学科技能集将对AML的生物学有深刻的了解，并与统计敏锐度和 设计新算法方面的专业知识。我的培训，经验和成功的科学贡献记录 让我非常适合推动这些项目的信息和分析方面的发展。