COMPREHENSIVE INFORMATIC ANALYSES OF AML GENOMES AND EPIGENOMES

摘要 十年的基因组研究揭示了急性胰腺炎基因组中出现的突变景观 骨髓性白血病（AML）。这些突变的一个子集已被确定为AML起始事件， 在造血干/祖细胞（HSPC）中产生生长优势。这导致了早期阶段的 疾病，但这些突变的作用机制知之甚少。这项研究计划将 通过产生全面的研究结果，研究原发性肿瘤和小鼠模型中的关键AML起始事件， 全基因组转录组和表观基因组数据（RNA-seq，亚硫酸氢盐-seq，ATAC-seq，单细胞RNA-seq， 等）。我的角色将是将这些庞大复杂的数据集转化为结论和可检验的假设 关于这些特定基因突变引发AML的精确分子机制。这样做将 需要开发新的算法和统计模型，这两个领域的生物信息学，我是 精通。然后，我们将利用这些知识开发新的疗法。 第二个关键问题集中在50%的AML患者，他们在治疗后最初经历完全缓解。 化疗，但最终复发。我们之前的工作表明，基因组测序通常可以 在这些患者的化疗后活检中鉴定出持续的白血病相关突变， 当他们处于形态缓解期时。这种突变清除的缺乏与以下风险密切相关： 复发，但不是绝对的预测：一些持续突变的患者经历了长时间的 缓解，而其他清除所有突变的人很快复发。剩下的两个重要问题 临床意义是：1）从额外的治疗后时间点的活检测序是否可以更好地定义 突变清除的轨迹，并提供额外的实用信息？2)可能是超深的 测序检测出了导致复发的残留疾病，从而促进了早期干预？ 共同领导了最初的突变清除研究，并编写了跟踪肿瘤克隆进化的工具 通过治疗，我有必要的专业知识来设计这些研究，并分析，可视化和解释 这些数据。我们正在开始临床试验，使用清除率来分配患者或多或少的侵略性， 治疗和提供更清晰的上述问题将是翻译这些发现的关键部分 进入强大的临床测试。 我的跨学科技能将对AML生物学的深刻理解与统计敏锐性结合起来， 设计新算法的专业知识。我的训练、经验和成功的科学贡献记录 使我特别适合推动这些项目的信息学和分析方面的发展。

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.